Internet Research Task Force (IRTF)                              F. GontRequest for Comments: 9415                                  SI6 NetworksCategory: Informational                                          I. ArceISSN: 2070-1721                                                Quarkslab                                                               July 2023           On the Generation of Transient Numeric IdentifiersAbstract   This document performs an analysis of the security and privacy   implications of different types of "transient numeric identifiers"   used in IETF protocols and tries to categorize them based on their   interoperability requirements and their associated failure severity   when such requirements are not met.  Subsequently, it provides advice   on possible algorithms that could be employed to satisfy the   interoperability requirements of each identifier category while   minimizing the negative security and privacy implications, thus   providing guidance to protocol designers and protocol implementers.   Finally, it describes a number of algorithms that have been employed   in real implementations to generate transient numeric identifiers and   analyzes their security and privacy properties.  This document is a   product of the Privacy Enhancements and Assessments Research Group   (PEARG) in the IRTF.Status of This Memo   This document is not an Internet Standards Track specification; it is   published for informational purposes.   This document is a product of the Internet Research Task Force   (IRTF).  The IRTF publishes the results of Internet-related research   and development activities.  These results might not be suitable for   deployment.  This RFC represents the consensus of the Privacy   Enhancements and Assessments Research Group of the Internet Research   Task Force (IRTF).  Documents approved for publication by the IRSG   are not candidates for any level of Internet Standard; see Section 2   of RFC 7841.   Information about the current status of this document, any errata,   and how to provide feedback on it may be obtained at   https://www.rfc-editor.org/info/rfc9415.Copyright Notice   Copyright (c) 2023 IETF Trust and the persons identified as the   document authors.  All rights reserved.   This document is subject to BCP 78 and the IETF Trust's Legal   Provisions Relating to IETF Documents   (https://trustee.ietf.org/license-info) in effect on the date of   publication of this document.  Please review these documents   carefully, as they describe your rights and restrictions with respect   to this document.Table of Contents   1.  Introduction   2.  Terminology   3.  Threat Model   4.  Issues with the Specification of Transient Numeric Identifiers   5.  Protocol Failure Severity   6.  Categorizing Transient Numeric Identifiers   7.  Common Algorithms for Transient Numeric Identifier Generation     7.1.  Category #1: Uniqueness (Soft Failure)     7.2.  Category #2: Uniqueness (Hard Failure)     7.3.  Category #3: Uniqueness, Stable within Context (Soft           Failure)     7.4.  Category #4: Uniqueness, Monotonically Increasing within           Context (Hard Failure)   8.  Common Vulnerabilities Associated with Transient Numeric           Identifiers     8.1.  Network Activity Correlation     8.2.  Information Leakage     8.3.  Fingerprinting     8.4.  Exploitation of the Semantics of Transient Numeric           Identifiers     8.5.  Exploitation of Collisions of Transient Numeric Identifiers     8.6.  Exploitation of Predictable Transient Numeric Identifiers           for Injection Attacks     8.7.  Cryptanalysis   9.  Vulnerability Assessment of Transient Numeric Identifiers     9.1.  Category #1: Uniqueness (Soft Failure)     9.2.  Category #2: Uniqueness (Hard Failure)     9.3.  Category #3: Uniqueness, Stable within Context (Soft           Failure)     9.4.  Category #4: Uniqueness, Monotonically Increasing within           Context (Hard Failure)   10. IANA Considerations   11. Security Considerations   12. References     12.1.  Normative References     12.2.  Informative References   Appendix A.  Algorithms and Techniques with Known Issues     A.1.  Predictable Linear Identifiers Algorithm     A.2.  Random-Increments Algorithm     A.3.  Reusing Identifiers Across Different Contexts   Acknowledgements   Authors' Addresses1.  Introduction   Networking protocols employ a variety of transient numeric   identifiers for different protocol objects, such as IPv4 and IPv6   Identification values [RFC0791] [RFC8200], IPv6 Interface Identifiers   (IIDs) [RFC4291], transport-protocol ephemeral port numbers   [RFC6056], TCP Initial Sequence Numbers (ISNs) [RFC9293], NTP   Reference IDs (REFIDs) [RFC5905], and DNS IDs [RFC1035].  These   identifiers typically have specific requirements (e.g., uniqueness   during a specified period of time) that must be satisfied such that   they do not result in negative interoperability implications and an   associated failure severity when such requirements are not met.      |  NOTE: Some documents refer to the DNS ID as the DNS "Query ID"      |  or "TxID".   For more than 30 years, a large number of implementations of IETF   protocols have been subject to a variety of attacks, with effects   ranging from Denial of Service (DoS) or data injection to information   leakages that could be exploited for pervasive monitoring [RFC7258].   The root cause of these issues has been, in many cases, the poor   selection of transient numeric identifiers in such protocols, usually   as a result of insufficient or misleading specifications.  While it   is generally trivial to identify an algorithm that can satisfy the   interoperability requirements of a given transient numeric   identifier, empirical evidence exists that doing so without   negatively affecting the security and/or privacy properties of the   aforementioned protocols is prone to error [RFC9414].   For example, implementations have been subject to security and/or   privacy issues resulting from:   *  predictable IPv4 or IPv6 Identification values (e.g., see      [Sanfilippo1998a], [RFC6274], and [RFC7739]),   *  predictable IPv6 IIDs (e.g., see [RFC7217], [RFC7707], and      [RFC7721]),   *  predictable transport-protocol ephemeral port numbers (e.g., see      [RFC6056] and [Silbersack2005]),   *  predictable TCP Initial Sequence Numbers (ISNs) (e.g., see      [Morris1985], [Bellovin1989], and [RFC6528]),   *  predictable initial timestamps in TCP timestamps options (e.g.,      see [TCPT-uptime] and [RFC7323]), and   *  predictable DNS IDs (see, e.g., [Schuba1993] and [Klein2007]).   Recent history indicates that, when new protocols are standardized or   new protocol implementations are produced, the security and privacy   properties of the associated transient numeric identifiers tend to be   overlooked, and inappropriate algorithms to generate such identifiers   are either suggested in the specifications or selected by   implementers.  As a result, advice in this area is warranted.   We note that the use of cryptographic techniques may readily mitigate   some of the issues arising from predictable transient numeric   identifiers.  For example, cryptographic authentication can readily   mitigate data injection attacks even in the presence of predictable   transient numeric identifiers (such as "sequence numbers").  However,   use of flawed algorithms (such as global counters) for generating   transient numeric identifiers could still result in information   leakages even when cryptographic techniques are employed.   This document contains a non-exhaustive survey of transient numeric   identifiers employed in various IETF protocols and aims to categorize   such identifiers based on their interoperability requirements and the   associated failure severity when such requirements are not met.   Subsequently, it provides advice on possible algorithms that could be   employed to satisfy the interoperability requirements of each   category while minimizing negative security and privacy implications.   Finally, it analyzes several algorithms that have been employed in   real implementations to meet such requirements and analyzes their   security and privacy properties.   This document represents the consensus of the Privacy Enhancements   and Assessments Research Group (PEARG).2.  Terminology   Transient Numeric Identifier:      A data object in a protocol specification that can be used to      definitely distinguish a protocol object (a datagram, network      interface, transport-protocol endpoint, session, etc.) from all      other objects of the same type, in a given context.  Transient      numeric identifiers are usually defined as a series of bits and      represented using integer values.  These identifiers are typically      dynamically selected, as opposed to statically assigned numeric      identifiers (see, e.g., [IANA-PROT]).  We note that different      transient numeric identifiers may have additional requirements or      properties depending on their specific use in a protocol.  We use      the term "transient numeric identifier" (or simply "numeric      identifier" or "identifier" as short forms) as a generic term to      refer to any data object in a protocol specification that      satisfies the identification property stated above.   Failure Severity:      The interoperability consequences of a failure to comply with the      interoperability requirements of a given identifier.  Severity      considers the worst potential consequence of a failure, determined      by the system damage and/or time lost to repair the failure.  In      this document, we define two types of failure severity: "soft      failure" and "hard failure".   Soft Failure:      A recoverable condition in which a protocol does not operate in      the prescribed manner but normal operation can be resumed      automatically in a short period of time.  For example, a simple      packet-loss event that is subsequently recovered with a packet      retransmission can be considered a soft failure.   Hard Failure:      A non-recoverable condition in which a protocol does not operate      in the prescribed manner or it operates with excessive degradation      of service.  For example, an established TCP connection that is      aborted due to an error condition constitutes, from the point of      view of the transport protocol, a hard failure, since it enters a      state from which normal operation cannot be resumed.3.  Threat Model   Throughout this document, we do not consider on-path attacks.  That   is, we assume the attacker does not have physical or logical access   to the system(s) being attacked and that the attacker can only   observe traffic explicitly directed to the attacker.  Similarly, an   attacker cannot observe traffic transferred between the sender and   the receiver(s) of a target protocol but may be able to interact with   any of these entities, including by, e.g., sending any traffic to   them to sample transient numeric identifiers employed by the target   hosts when communicating with the attacker.   For example, when analyzing vulnerabilities associated with TCP   Initial Sequence Numbers (ISNs), we consider the attacker is unable   to capture network traffic corresponding to a TCP connection between   two other hosts.  However, we consider the attacker is able to   communicate with any of these hosts (e.g., establish a TCP connection   with any of them) to, e.g., sample the TCP ISNs employed by these   hosts when communicating with the attacker.   Similarly, when considering host-tracking attacks based on IPv6   Interface Identifiers, we consider an attacker may learn the IPv6   address employed by a victim host if, e.g., the address becomes   exposed as a result of the victim host communicating with an   attacker-operated server.  Subsequently, an attacker may perform   host-tracking by probing a set of target addresses composed by a set   of target prefixes and the IPv6 Interface Identifier originally   learned by the attacker.  Alternatively, an attacker may perform   host-tracking if, e.g., the victim host communicates with an   attacker-operated server as it moves from one location to another,   thereby exposing its configured addresses.  We note that none of   these scenarios require the attacker observe traffic not explicitly   directed to the attacker.4.  Issues with the Specification of Transient Numeric Identifiers   While assessing IETF protocol specifications regarding the use of   transient numeric identifiers, we have found that most of the issues   discussed in this document arise as a result of one of the following   conditions:   *  protocol specifications that under specify their transient numeric      identifiers   *  protocol specifications that over specify their transient numeric      identifiers   *  protocol implementations that simply fail to comply with the      specified requirements   A number of IETF protocol specifications under specified their   transient numeric identifiers, thus leading to implementations that   were vulnerable to numerous off-path attacks.  Examples of them are   the specification of TCP local ports in [RFC0793] or the   specification of the DNS ID in [RFC1035].      |  NOTE: The TCP local port in an active OPEN request is commonly      |  known as the "ephemeral port" of the corresponding TCP      |  connection [RFC6056].   On the other hand, there are a number of IETF protocol specifications   that over specify some of their associated transient numeric   identifiers.  For example, [RFC4291] essentially overloads the   semantics of IPv6 Interface Identifiers (IIDs) by embedding link-   layer addresses in the IPv6 IIDs when the interoperability   requirement of uniqueness could be achieved in other ways that do not   result in negative security and privacy implications [RFC7721].   Similarly, [RFC2460] suggests the use of a global counter for the   generation of Identification values when the interoperability   requirement of uniqueness per {IPv6 Source Address, IPv6 Destination   Address} could be achieved with other algorithms that do not result   in negative security and privacy implications [RFC7739].   Finally, there are protocol implementations that simply fail to   comply with existing protocol specifications.  For example, some   popular operating systems still fail to implement transport-protocol   ephemeral port randomization, as recommended in [RFC6056], or TCP   Initial Sequence Number randomization, as recommended in [RFC9293].5.  Protocol Failure Severity   Section 2 defines the concept of "failure severity", along with two   types of failure severities that we employ throughout this document:   soft and hard.   Our analysis of the severity of a failure is performed from the point   of view of the protocol in question.  However, the corresponding   severity on the upper protocol (or application) might not be the same   as that of the protocol in question.  For example, a TCP connection   that is aborted might or might not result in a hard failure of the   upper application, i.e., if the upper application can establish a new   TCP connection without any impact on the application, a hard failure   at the TCP protocol may have no severity at the application layer.   On the other hand, if a hard failure of a TCP connection results in   excessive degradation of service at the application layer, it will   also result in a hard failure at the application.6.  Categorizing Transient Numeric Identifiers   This section includes a non-exhaustive survey of transient numeric   identifiers, which are representative of all the possible   combinations of interoperability requirements and failure severities   found in popular protocols of different layers.  Additionally, it   proposes a number of categories that can accommodate these   identifiers based on their interoperability requirements and their   associated failure severity (soft or hard).      |  NOTE: All other transient numeric identifiers that were      |  analyzed as part of this effort could be accommodated into one      |  of the existing categories from Table 1.   +===============+===============================+==================+   |   Identifier  | Interoperability Requirements | Failure Severity |   +===============+===============================+==================+   |    IPv6 ID    |  Uniqueness (for IPv6 address |  Soft/Hard (1)   |   |               |             pair)             |                  |   +---------------+-------------------------------+------------------+   |    IPv6 IID   | Uniqueness (and stable within |     Soft (3)     |   |               |        IPv6 prefix) (2)       |                  |   +---------------+-------------------------------+------------------+   |    TCP ISN    |  Monotonically increasing (4) |     Hard (4)     |   +---------------+-------------------------------+------------------+   |  TCP initial  |  Monotonically increasing (5) |     Hard (5)     |   |   timestamp   |                               |                  |   +---------------+-------------------------------+------------------+   | TCP ephemeral |   Uniqueness (for connection  |       Hard       |   |      port     |              ID)              |                  |   +---------------+-------------------------------+------------------+   |   IPv6 Flow   |           Uniqueness          |     None (6)     |   |     Label     |                               |                  |   +---------------+-------------------------------+------------------+   |     DNS ID    |           Uniqueness          |     None (7)     |   +---------------+-------------------------------+------------------+             Table 1: Survey of Transient Numeric Identifiers   NOTE:   (1)  While a single collision of IPv6 Identification (ID) values        would simply lead to a single packet drop (and hence, a "soft"        failure), repeated collisions at high data rates might result in        self-propagating collisions of IPv6 IDs, thus possibly leading        to a hard failure [RFC4963].   (2)  While the interoperability requirements are simply that the        Interface Identifier results in a unique IPv6 address, for        operational reasons, it is typically desirable that the        resulting IPv6 address (and hence, the corresponding Interface        Identifier) be stable within each network [RFC7217] [RFC8064].   (3)  While IPv6 Interface Identifiers must result in unique IPv6        addresses, IPv6 Duplicate Address Detection (DAD) [RFC4862]        allows for the detection of duplicate addresses, and hence, such        Interface Identifier collisions can be recovered.   (4)  In theory, there are no interoperability requirements for TCP        Initial Sequence Numbers (ISNs), since the TIME-WAIT state and        TCP's "quiet time" concept take care of old segments from        previous incarnations of a connection.  However, a widespread        optimization allows for a new incarnation of a previous        connection to be created if the ISN of the incoming SYN is        larger than the last sequence number seen in that direction for        the previous incarnation of the connection.  Thus, monotonically        increasing TCP ISNs allow for such optimization to work as        expected [RFC6528] and can help avoid connection-establishment        failures.   (5)  Strictly speaking, there are no interoperability requirements        for the *initial* TCP timestamp employed by a TCP instance        (i.e., the TS Value (TSval) in a segment with the SYN bit set).        However, some TCP implementations allow a new incarnation of a        previous connection to be created if the TSval of the incoming        SYN is larger than the last TSval seen in that direction for the        previous incarnation of the connection (please see [RFC6191]).        Thus, monotonically increasing TCP initial timestamps (across        connections to the same endpoint) allow for such optimization to        work as expected [RFC6191] and can help avoid connection-        establishment failures.   (6)  The IPv6 Flow Label [RFC6437], along with the IPv6 Source        Address and the IPv6 Destination Address, is typically employed        for load sharing [RFC7098].  Reuse of a Flow Label value for the        same set {Source Address, Destination Address} would typically        cause both flows to be multiplexed onto the same link.  However,        as long as this does not occur deterministically, it will not        result in any negative implications.   (7)  DNS IDs are employed, together with the IP Source Address, the        IP Destination Address, the transport-protocol Source Port, and        the transport-protocol Destination Port, to match DNS requests        and responses.  However, since an implementation knows which DNS        requests were sent for that set of {IP Source Address, IP        Destination Address, transport-protocol Source Port, transport-        protocol Destination Port, DNS ID}, a collision of DNS IDs would        result, if anything, in a small performance penalty (the        response would nevertheless be discarded when it is found that        it does not answer the query sent in the corresponding DNS        query).   Based on the survey above, we can categorize identifiers as follows:   +=======+======================================+====================+   | Cat # |               Category               |   Sample Numeric   |   |       |                                      |        IDs         |   +=======+======================================+====================+   |   1   |      Uniqueness (soft failure)       | IPv6 Flow L., DNS  |   |       |                                      |         ID         |   +-------+--------------------------------------+--------------------+   |   2   |      Uniqueness (hard failure)       |    IPv6 ID, TCP    |   |       |                                      |   ephemeral port   |   +-------+--------------------------------------+--------------------+   |   3   |  Uniqueness, stable within context   |      IPv6 IID      |   |       |            (soft failure)            |                    |   +-------+--------------------------------------+--------------------+   |   4   | Uniqueness, monotonically increasing |    TCP ISN, TCP    |   |       |    within context (hard failure)     | initial timestamp  |   +-------+--------------------------------------+--------------------+                       Table 2: Identifier Categories   We note that Category #4 could be considered a generalized case of   Category #3, in which a monotonically increasing element is added to   a stable (within context) element, such that the resulting   identifiers are monotonically increasing within a specified context.   That is, the same algorithm could be employed for both #3 and #4,   given appropriate parameters.7.  Common Algorithms for Transient Numeric Identifier Generation   The following subsections describe some sample algorithms that can be   employed for generating transient numeric identifiers for each of the   categories above while mitigating the vulnerabilities analyzed in   Section 8 of this document.   All of the variables employed in the algorithms of the following   subsections are of "unsigned integer" type, except for the "retry"   variable, which is of (signed) "integer" type.7.1.  Category #1: Uniqueness (Soft Failure)   The requirement of uniqueness with a soft failure severity can be   complied with a Pseudorandom Number Generator (PRNG).      |  NOTE: Please see [RFC4086] regarding randomness requirements      |  for security.   While most systems provide access to a PRNG, many of such PRNG   implementations are not cryptographically secure and therefore might   be statistically biased or subject to adversarial influence.  For   example, ISO C [C11] rand(3) implementations are not   cryptographically secure.      |  NOTE: Section 7.1 ("Uniform Deviates") of [Press1992] discusses      |  the underlying issues affecting ISO C [C11] rand(3)      |  implementations.   On the other hand, a number of systems provide an interface to a   Cryptographically Secure PRNG (CSPRNG) [RFC4086] [RFC8937], which   guarantees high entropy, unpredictability, and good statistical   distribution of the random values generated.  For example, GNU/   Linux's CSPRNG implementation is available via the getentropy(3)   interface [GETENTROPY], while OpenBSD's CSPRNG implementation is   available via the arc4random(3) and arc4random_uniform(3) interfaces   [ARC4RANDOM].  Where available, these CSPRNGs should be preferred   over, e.g., POSIX [POSIX] random(3) or ISO C [C11] rand(3)   implementations.   In scenarios where a CSPRNG is not readily available to select   transient numeric identifiers of Category #1, a security and privacy   assessment of employing a regular PRNG should be performed,   supporting the implementation decision.      |  NOTE: [Aumasson2018], [Press1992], and [Knuth1983] discuss      |  theoretical and practical aspects of pseudorandom number      |  generation and provide guidance on how to evaluate PRNGs.   We note that, since the premise is that collisions of transient   numeric identifiers of this category only lead to soft failures, in   many cases, the algorithm might not need to check the suitability of   a selected identifier (i.e., the suitable_id() function, described   below, could always return "true").   In scenarios where, e.g., simultaneous use of a given numeric   identifier is undesirable and an implementation detects such   condition, the implementation may opt to select the next available   identifier in the same sequence or select another random number.   Section 7.1.1 is an implementation of the former strategy, while   Section 7.1.2 is an implementation of the latter.  Typically, the   algorithm in Section 7.1.2 results in a more uniform distribution of   the generated transient numeric identifiers.  However, for transient   numeric identifiers where an implementation typically keeps local   state about unsuitable/used identifiers, the algorithm in   Section 7.1.2 may require many more iterations than the algorithm in   Section 7.1.1 to generate a suitable transient numeric identifier.   This will usually be affected by the current usage ratio of transient   numeric identifiers (i.e., the number of numeric identifiers   considered suitable / total number of numeric identifiers) and other   parameters.  Therefore, in such cases, many implementations tend to   prefer the algorithm in Section 7.1.1 over the algorithm in   Section 7.1.2.7.1.1.  Simple Randomization Algorithm       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       next_id = min_id + (random() % id_range);       retry = id_range;       do {           if (suitable_id(next_id)) {               return next_id;           }           if (next_id == max_id) {               next_id = min_id;           } else {               next_id++;           }           retry--;       } while (retry > 0);       return ERROR;   NOTE:      random() is a PRNG that returns a pseudorandom unsigned integer      number of appropriate size.  Beware that "adapting" the length of      the output of random() with a modulo operator (e.g., C language's      "%") may change the distribution of the PRNG.  To preserve a      uniform distribution, the rejection sampling technique      [Romailler2020] can be used.      suitable_id() is a function that checks, if possible and      desirable, whether a candidate numeric identifier is suitable      (e.g., whether it is in use or has been recently employed).      Depending on how/where the numeric identifier is used, it may or      may not be possible (or even desirable) to check whether the      numeric identifier is suitable.      All the variables (in this algorithm and all the others algorithms      discussed in this document) are unsigned integers.   When an identifier is found to be unsuitable, this algorithm selects   the next available numeric identifier in sequence.  Thus, even when   this algorithm selects numeric identifiers randomly, it is biased   towards the first available numeric identifier after a sequence of   unavailable numeric identifiers.  For example, if this algorithm is   employed for transport-protocol ephemeral port randomization   [RFC6056] and the local list of unsuitable port numbers (e.g.,   registered port numbers that should not be used for ephemeral ports)   is significant, an attacker may actually have a significantly better   chance of guessing an ephemeral port number.   Assuming the randomness requirements for the PRNG are met (see   [RFC4086]), this algorithm does not suffer from any of the issues   discussed in Section 8.7.1.2.  Another Simple Randomization Algorithm   The following pseudocode illustrates another algorithm for selecting   a random transient numeric identifier where, in the event a selected   identifier is found to be unsuitable (e.g., already in use), another   identifier is randomly selected:       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       retry = id_range;       do {           next_id = min_id + (random() % id_range);           if (suitable_id(next_id)) {               return next_id;           }           retry--;       } while (retry > 0);       return ERROR;   NOTE:      random() is a PRNG that returns a pseudorandom unsigned integer      number of appropriate size.  Beware that "adapting" the length of      the output of random() with a modulo operator (e.g., C language's      "%") may change the distribution of the PRNG.  To preserve a      uniform distribution, the rejection sampling technique      [Romailler2020] can be used.      suitable_id() is a function that checks, if possible and      desirable, whether a candidate numeric identifier is suitable      (e.g., if it is not already in use).  Depending on how/where the      numeric identifier is used, it may or may not be possible (or even      desirable) to check whether the numeric identifier is in use (or      whether it has been recently employed).   When an identifier is found to be unsuitable, this algorithm selects   another random numeric identifier.  Thus, this algorithm might be   unable to select a transient numeric identifier (i.e., return   "ERROR"), even if there are suitable identifiers available, in cases   where a large number of identifiers are found to be unsuitable (e.g.,   "in use").   Assuming the randomness requirements for the PRNG are met (see   [RFC4086]), this algorithm does not suffer from any of the issues   discussed in Section 8.7.2.  Category #2: Uniqueness (Hard Failure)   One of the most trivial approaches for generating a unique transient   numeric identifier (with a hard failure severity) is to reduce the   identifier reuse frequency by generating the numeric identifiers with   a monotonically increasing function (e.g., linear).  As a result, any   of the algorithms described in Section 7.4 ("Category #4: Uniqueness,   Monotonically Increasing within Context (Hard Failure)") can be   readily employed for complying with the requirements of this   transient numeric identifier category.   In cases where suitability (e.g., uniqueness) of the selected   identifiers can be definitely assessed by the local system, any of   the algorithms described in Section 7.1 ("Category #1: Uniqueness   (Soft Failure)") can be readily employed for complying with the   requirements of this numeric identifier category.      |  NOTE: In the case of, e.g., TCP ephemeral ports or TCP ISNs, a      |  transient numeric identifier that might seem suitable from the      |  perspective of the local system might actually be unsuitable      |  from the perspective of the remote system (e.g., because there      |  is state associated with the selected identifier at the remote      |  system).  Therefore, in such cases, it is not possible to      |  employ the algorithms from Section 7.1 ("Category #1:      |  Uniqueness (Soft Failure)").7.3.  Category #3: Uniqueness, Stable within Context (Soft Failure)   The goal of the following algorithm is to produce identifiers that   are stable for a given context (identified by "CONTEXT") but that   change when the aforementioned context changes.   In order to avoid storing the transient numeric identifiers computed   for each CONTEXT in memory, the following algorithm employs a   calculated technique (as opposed to keeping state in memory) to   generate a stable transient numeric identifier for each given   context.       /* Transient Numeric ID selection function  */       id_range = max_id - min_id + 1;       retry = 0;       do {           offset = F(CONTEXT, retry, secret_key);           next_id = min_id + (offset % id_range);           if (suitable_id(next_id)) {               return next_id;           }           retry++;       } while (retry <= MAX_RETRIES);       return ERROR;   NOTE:      CONTEXT is the concatenation of all the elements that define a      given context.      F() is a pseudorandom function (PRF).  It must not be computable      from the outside (without knowledge of the secret key).  F() must      also be difficult to reverse, such that it resists attempts to      obtain the secret key, even when given samples of the output of      F() and knowledge or control of the other input parameters.  F()      should produce an output of at least as many bits as required for      the transient numeric identifier.  SipHash-2-4 (128-bit key,      64-bit output) [SipHash] and BLAKE3 (256-bit key, arbitrary-length      output) [BLAKE3] are two possible options for F().  Alternatively,      F() could be implemented with a keyed hash message authentication      code (HMAC) [RFC2104].  HMAC-SHA-256 [FIPS-SHS] would be one      possible option for such implementation alternative.  Note: Use of      HMAC-MD5 [RFC1321] or HMAC-SHA1 [FIPS-SHS] are not recommended for      F() [RFC6151] [RFC6194].  The result of F() is no more secure than      the secret key, and therefore, "secret_key" must be unknown to the      attacker and must be of a reasonable length. "secret_key" must      remain stable for a given CONTEXT, since otherwise, the numeric      identifiers generated by this algorithm would not have the desired      stability properties (i.e., stable for a given CONTEXT).  In most      cases, "secret_key" should be selected with a PRNG (see [RFC4086]      for recommendations on choosing secrets) at an appropriate time      and stored in stable or volatile storage (as necessary) for future      use.      suitable_id() checks whether a candidate numeric identifier has      suitable uniqueness properties.   In this algorithm, the function F() provides a stateless and stable   per-CONTEXT offset, where CONTEXT is the concatenation of all the   elements that define the given context.   For example, if this algorithm is expected to produce IPv6 IIDs that   are unique per network interface and Stateless Address   Autoconfiguration (SLAAC) prefix, CONTEXT should be the concatenation   of, e.g., the network interface index and the SLAAC autoconfiguration   prefix (please see [RFC7217] for an implementation of this algorithm   for generation of stable IPv6 addresses).   The result of F() is stored in the variable "offset", which may take   any value within the storage type range, since we are restricting the   resulting identifier to be in the range [min_id, max_id] in a similar   way as in the algorithm described in Section 7.1.1.   As noted above, suitable_id() checks whether a candidate numeric   identifier has suitable uniqueness properties.  Collisions (i.e., an   identifier that is not unique) are recovered by incrementing the   "retry" variable and recomputing F(), up to a maximum of MAX_RETRIES   times.  However, recovering from collisions will usually result in   identifiers that fail to remain constant for the specified context.   This is normally acceptable when the probability of collisions is   small, as in the case of, e.g., IPv6 IIDs resulting from SLAAC   [RFC7217] [RFC8981].   For obvious reasons, the transient numeric identifiers generated with   this algorithm allow for network activity correlation and   fingerprinting within "CONTEXT".  However, this is essentially a   design goal of this category of transient numeric identifiers.7.4.  Category #4: Uniqueness, Monotonically Increasing within Context      (Hard Failure)7.4.1.  Per-Context Counter Algorithm   One possible way of selecting unique monotonically increasing   identifiers (per context) is to employ a per-context counter.  Such   an algorithm could be described as follows:       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       retry = id_range;       id_inc = increment() % id_range;       if( (next_id = lookup_counter(CONTEXT)) == ERROR){            next_id = min_id + random() % id_range;       }       do {           if ( (max_id - next_id) >= id_inc){               next_id = next_id + id_inc;           }           else {               next_id = min_id + id_inc - (max_id - next_id);           }           if (suitable_id(next_id)){               store_counter(CONTEXT, next_id);               return next_id;           }           retry = retry - id_inc;       } while (retry > 0);       return ERROR;   NOTE:      CONTEXT is the concatenation of all the elements that define a      given context.      increment() returns a small integer that is employed to increment      the current counter value to obtain the next transient numeric      identifier.  This value must be larger than or equal to 1, and      much smaller than the number of possible values for the numeric      identifiers (i.e., "id_range").  Most implementations of this      algorithm employ a constant increment of 1.  Using a value other      than 1 can help mitigate some information leakages (please see      below) at the expense of a possible increase in the numeric      identifier reuse frequency.  The code above makes sure that the      increment employed in the algorithm (id_inc) is always smaller      than the number of possible values for the numeric identifiers      (i.e., "max_id - min_d + 1").  However, as noted above, this value      must also be much smaller than the number of possible values for      the numeric identifiers.      lookup_counter() is a function that returns the current counter      for a given context or an error condition if that counter does not      exist.      random() is a PRNG that returns a pseudorandom unsigned integer      number of appropriate size.  Beware that "adapting" the length of      the output of random() with a modulo operator (e.g., C language's      "%") may change the distribution of the PRNG.  To preserve a      uniform distribution, the rejection sampling technique      [Romailler2020] can be used.      store_counter() is a function that saves a counter value for a      given context.      suitable_id() checks whether a candidate numeric identifier has      suitable uniqueness properties.   Essentially, whenever a new identifier is to be selected, the   algorithm checks whether a counter for the corresponding context   exists.  If it does, the value of such counter is incremented to   obtain the new transient numeric identifier, and the counter is   updated.  If no counter exists for such context, a new counter is   created and initialized to a random value and used as the selected   transient numeric identifier.  This algorithm produces a per-context   counter, which results in one monotonically increasing function for   each context.  Since each counter is initialized to a random value,   the resulting values are unpredictable by an off-path attacker.   The choice of id_inc has implications on both the security and   privacy properties of the resulting identifiers and also on the   corresponding interoperability properties.  On one hand, minimizing   the increments generally minimizes the identifier reuse frequency,   albeit at increased predictability.  On the other hand, if the   increments are randomized, predictability of the resulting   identifiers is reduced, and the information leakage produced by   global constant increments is mitigated.  However, using larger   increments than necessary can result in higher numeric identifier   reuse frequency.   This algorithm has the following drawbacks:   *  It requires an implementation to store each per-context counter in      memory.  If, as a result of resource management, the counter for a      given context must be removed, the last transient numeric      identifier value used for that context will be lost.  Thus, if an      identifier subsequently needs to be generated for the same      context, the corresponding counter will need to be recreated and      reinitialized to a random value, thus possibly leading to reuse/      collision of numeric identifiers.   *  Keeping one counter for each possible "context" may in some cases      be considered too onerous in terms of memory requirements.   Otherwise, the identifiers produced by this algorithm do not suffer   from the other issues discussed in Section 8.7.4.2.  Simple PRF-Based Algorithm   The goal of this algorithm is to produce monotonically increasing   transient numeric identifiers (for each given context) with a   randomized initial value.  For example, if the identifiers being   generated must be monotonically increasing for each {Source Address,   Destination Address} set, then each possible combination of {Source   Address, Destination Address} should have a separate monotonically   increasing sequence that starts at a different random value.   Instead of maintaining a per-context counter (as in the algorithm   from Section 7.4.1), the following algorithm employs a calculated   technique to maintain a random offset for each possible context.       /* Initialization code */       counter = 0;       /* Transient Numeric ID selection function  */       id_range = max_id - min_id + 1;       id_inc = increment() % id_range;       offset = F(CONTEXT, secret_key);       retry = id_range;       do {           next_id = min_id + (offset + counter) % id_range;           counter = counter + id_inc;           if (suitable_id(next_id)) {               return next_id;           }           retry = retry - id_inc;       } while (retry > 0);       return ERROR;   NOTE:      CONTEXT is the concatenation of all the elements that define a      given context.  For example, if this algorithm is expected to      produce identifiers that are monotonically increasing for each set      {Source Address, Destination Address}, CONTEXT should be the      concatenation of Source Address and Destination Address.      increment() has the same properties and requirements as those      specified for increment() in Section 7.4.1.      F() is a PRF, with the same properties as those specified for F()      in Section 7.3.      suitable_id() checks whether a candidate numeric identifier has      suitable uniqueness properties.   In the algorithm above, the function F() provides a stateless,   stable, and unpredictable offset for each given context (as   identified by "CONTEXT").  Both the "offset" and "counter" variables   may take any value within the storage type range since we are   restricting the resulting identifier to be in the range [min_id,   max_id] in a similar way as in the algorithm described in   Section 7.1.1.  This allows us to simply increment the "counter"   variable and rely on the unsigned integer to wrap around.   The result of F() is no more secure than the secret key, and   therefore, "secret_key" must be unknown to the attacker and must be   of a reasonable length. "secret_key" must remain stable for a given   CONTEXT, since otherwise, the numeric identifiers generated by this   algorithm would not have the desired properties (i.e., monotonically   increasing for a given CONTEXT).  In most cases, "secret_key" should   be selected with a PRNG (see [RFC4086] for recommendations on   choosing secrets) at an appropriate time and stored in stable or   volatile storage (as necessary) for future use.   It should be noted that, since this algorithm uses a global counter   ("counter") for selecting identifiers (i.e., all counters share the   same increment space), this algorithm results in an information   leakage (as described in Section 8.2).  For example, if this   algorithm was used for selecting TCP ephemeral ports and an attacker   could force a client to periodically establish a new TCP connection   to an attacker-controlled system (or through an attacker-observable   routing path), the attacker could subtract consecutive Source Port   values to obtain the number of outgoing TCP connections established   globally by the victim host within that time period (up to wrap-   around issues and five-tuple collisions, of course).  This   information leakage could be partially mitigated by employing small   random values for the increments (i.e., increment() function),   instead of having increment() return the constant "1".   We nevertheless note that an improved mitigation of this information   leakage could be more successfully achieved by employing the   algorithm from Section 7.4.3, instead.7.4.3.  Double-PRF Algorithm   A trade-off between maintaining a single global "counter" variable   and maintaining 2**N "counter" variables (where N is the width of the   result of F()) could be achieved as follows.  The system would keep   an array of TABLE_LENGTH values, which would provide a separation of   the increment space into multiple buckets.  This improvement could be   incorporated into the algorithm from Section 7.4.2 as follows:       /* Initialization code */       for(i = 0; i < TABLE_LENGTH; i++) {           table[i] = random();       }       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       id_inc = increment() % id_range;       offset = F(CONTEXT, secret_key1);       index = G(CONTEXT, secret_key2) % TABLE_LENGTH;       retry = id_range;       do {           next_id = min_id + (offset + table[index]) % id_range;           table[index] = table[index] + id_inc;           if (suitable_id(next_id)) {               return next_id;           }          retry = retry - id_inc;       } while (retry > 0);       return ERROR;   NOTE:      increment() has the same properties and requirements as those      specified for increment() in Section 7.4.1.      Both F() and G() are PRFs, with the same properties as those      required for F() in Section 7.3.  The results of F() and G() are      no more secure than their respective secret keys ("secret_key1"      and "secret_key2", respectively), and therefore, both secret keys      must be unknown to the attacker and must be of a reasonable      length.  Both secret keys must remain stable for the given      CONTEXT, since otherwise, the transient numeric identifiers      generated by this algorithm would not have the desired properties      (i.e., monotonically increasing for a given CONTEXT).  In most      cases, both secret keys should be selected with a PRNG (see      [RFC4086] for recommendations on choosing secrets) at an      appropriate time and stored in stable or volatile storage (as      necessary) for future use.      "table[]" could be initialized with random values, as indicated by      the initialization code in the pseudocode above.   The "table[]" array assures that successive transient numeric   identifiers for a given context will be monotonically increasing.   Since the increment space is separated into TABLE_LENGTH different   spaces, the identifier reuse frequency will be (probabilistically)   lower than that of the algorithm in Section 7.4.2.  That is, the   generation of an identifier for one given context will not   necessarily result in increments in the identifier sequence of other   contexts.  It is interesting to note that the size of "table[]" does   not limit the number of different identifier sequences but rather   separates the *increment space* into TABLE_LENGTH different spaces.   The selected transient numeric identifier sequence will be obtained   by adding the corresponding entry from "table[]" to the value in the   "offset" variable, which selects the actual identifier sequence space   (as in the algorithm from Section 7.4.2).   An attacker can perform traffic analysis for any "increment space"   (i.e., context) into which the attacker has "visibility" -- namely,   the attacker can force a system to generate identifiers for   G(CONTEXT, secret_key2), where the result of G() identifies the   target "increment space".  However, the attacker's ability to perform   traffic analysis is very reduced when compared to the simple PRF-   based identifiers (described in Section 7.4.2) and the predictable   linear identifiers (described in Appendix A.1).  Additionally, an   implementation can further limit the attacker's ability to perform   traffic analysis by further separating the increment space (that is,   using a larger value for TABLE_LENGTH) and/or by randomizing the   increments (i.e., increment() returning a small random number as   opposed to the constant "1").   Otherwise, this algorithm does not suffer from the issues discussed   in Section 8.8.  Common Vulnerabilities Associated with Transient Numeric Identifiers8.1.  Network Activity Correlation   An identifier that is predictable within a given context allows for   network activity correlation within that context.   For example, a stable IPv6 Interface Identifier allows for network   activity to be correlated within the context in which the Interface   Identifier is stable [RFC7721].  A stable per-network IPv6 Interface   Identifier (as in [RFC7217]) allows for network activity correlation   within a network, whereas a constant IPv6 Interface Identifier (which   remains constant across networks) allows not only network activity   correlation within the same network but also across networks ("host-   tracking").   Similarly, an implementation that generates TCP ISNs with a global   counter could allow for fingerprinting and network activity   correlation across networks, since an attacker could passively infer   the identity of the victim based on the TCP ISNs employed for   subsequent communication instances.  Similarly, an implementation   that generates predictable IPv6 Identification values could be   subject to fingerprinting attacks (see, e.g., [Bellovin2002]).8.2.  Information Leakage   Transient numeric identifiers that result in specific patterns can   produce an information leakage to other communicating entities.  For   example, it is common to generate transient numeric identifiers with   an algorithm such as:              ID = offset(CONTEXT) + mono(CONTEXT);   This generic expression generates identifiers by adding a   monotonically increasing function (e.g., linear) to a randomized   offset. offset() is constant within a given context, whereas mono()   produces a monotonically increasing sequence for the given context.   Identifiers generated with this expression will generally be   predictable within CONTEXT.   The predictability of mono(), irrespective of the predictability of   offset(), can leak information that may be of use to attackers.  For   example, a node that selects transport-protocol ephemeral port   numbers, as in:              ephemeral_port = offset(IP_Dst_Addr) + mono()   that is, with a per-destination offset but a global mono() function   (e.g., a global counter), will leak information about the total   number of outgoing connections that have been issued by the   vulnerable implementation.   Similarly, a node that generates IPv6 Identification values as in:              ID = offset(IP_Src_Addr, IP_Dst_Addr) + mono()   will leak out information about the total number of fragmented   packets that have been transmitted by the vulnerable implementation.   The vulnerabilities described in [Sanfilippo1998a],   [Sanfilippo1998b], and [Sanfilippo1999] are all associated with the   use of a global mono() function (i.e., with a global and constant   "CONTEXT") -- particularly when it is a linear function (constant   increments of 1).   Predicting transient numeric identifiers can be of help for other   types of attacks.  For example, predictable TCP ISNs can open the   door to trivial connection-reset and data injection attacks (see   Section 8.6).8.3.  Fingerprinting   Fingerprinting is the capability of an attacker to identify or   reidentify a visiting user, user agent, or device via configuration   settings or other observable characteristics.  Observable protocol   objects and characteristics can be employed to identify/reidentify   various entities.  These entities can range from the underlying   hardware or operating system (OS) (vendor, type, and version) to the   user.  [EFF] illustrates web-browser-based fingerprinting, but   similar techniques can be applied at other layers and protocols,   whether alternatively or in conjunction with it.   Transient numeric identifiers are one of the observable protocol   components that could be leveraged for fingerprinting purposes.  That   is, an attacker could sample transient numeric identifiers to infer   the algorithm (and its associated parameters, if any) for generating   such identifiers, possibly revealing the underlying OS vendor, type,   and version.  This information could possibly be further leveraged in   conjunction with other fingerprinting techniques and sources.   Evasion of protocol-stack fingerprinting can prove to be a very   difficult task, i.e., most systems make use of a wide variety of   protocols, each of which have a large number of parameters that can   be set to arbitrary values or generated with a variety of algorithms   with multiple parameters.      |  NOTE: General protocol-based fingerprinting is discussed in      |  [RFC6973], along with guidelines to mitigate the associated      |  vulnerability.  [Fyodor1998] and [Fyodor2006] are classic      |  references on OS detection via TCP/IP stack fingerprinting.      |  Network Mapper [nmap] is probably the most popular tool for      |  remote OS identification via active TCP/IP stack      |  fingerprinting. p0f [Zalewski2012], on the other hand, is a      |  tool for performing remote OS detection via passive TCP/IP      |  stack fingerprinting.  Finally, [TBIT] is a TCP fingerprinting      |  tool that aims at characterizing the behavior of a remote TCP      |  peer based on active probes, which has been widely used in the      |  research community.   Algorithms that, from the perspective of an observer (e.g., the   legitimate communicating peer), result in specific values or patterns   will allow for at least some level of fingerprinting.  For example,   the algorithm from Section 7.3 will typically allow fingerprinting   within the context where the resulting identifiers are stable.   Similarly, the algorithms from Section 7.4 will result in   monotonically increasing sequences within a given context, thus   allowing for at least some level of fingerprinting (when the other   communicating entity can correlate different sampled identifiers as   belonging to the same monotonically increasing sequence).   Thus, where possible, algorithms from Section 7.1 should be preferred   over algorithms that result in specific values or patterns.8.4.  Exploitation of the Semantics of Transient Numeric Identifiers   Identifiers that are not semantically opaque tend to be more   predictable than semantically opaque identifiers.  For example, a   Media Access Control (MAC) address contains an Organizationally   Unique Identifier (OUI), which may identify the vendor that   manufactured the corresponding network interface card.  This can be   leveraged by an attacker trying to "guess" MAC addresses, who has   some knowledge about the possible Network Interface Card (NIC)   vendor.   [RFC7707] discusses a number of techniques to reduce the search space   when performing IPv6 address-scanning attacks by leveraging the   semantics of IPv6 IIDs.8.5.  Exploitation of Collisions of Transient Numeric Identifiers   In many cases, the collision of transient network identifiers can   have a hard failure severity (or result in a hard failure severity if   an attacker can cause multiple collisions deterministically, one   after another).  For example, predictable IP Identification values   open the door to Denial of Service (DoS) attacks (see, e.g.,   [RFC5722].).8.6.  Exploitation of Predictable Transient Numeric Identifiers for      Injection Attacks   Some protocols rely on "sequence numbers" for the validation of   incoming packets.  For example, TCP employs sequence numbers for   reassembling TCP segments, while IPv4 and IPv6 employ Identification   values for reassembling IPv4 and IPv6 fragments (respectively).   Lacking built-in cryptographic mechanisms for validating packets,   these protocols are therefore vulnerable to on-path data (see, e.g.,   [Joncheray1995]) and/or control-information (see, e.g., [RFC4953] and   [RFC5927]) injection attacks.  The extent to which these protocols   may resist off-path (i.e., "blind") injection attacks depends on   whether the associated "sequence numbers" are predictable and the   effort required to successfully predict a valid "sequence number"   (see, e.g., [RFC4953] and [RFC5927]).   We note that the use of unpredictable "sequence numbers" is a   completely ineffective mitigation for on-path injection attacks and   also a mostly ineffective mitigation for off-path (i.e., "blind")   injection attacks.  However, many legacy protocols (such as TCP) do   not incorporate cryptographic mitigations as part of the core   protocol but rather as optional features (see, e.g., [RFC5925]), if   available at all.  Additionally, ad hoc use of cryptographic   mitigations might not be sufficient to relieve a protocol   implementation of generating appropriate transient numeric   identifiers.  For example, use of the Transport Layer Security (TLS)   protocol [RFC8446] with TCP will protect the application protocol but   will not help to mitigate, e.g., TCP-based connection-reset attacks   (see, e.g., [RFC4953]).  Similarly, use of SEcure Neighbor Discovery   (SEND) [RFC3971] will still imply reliance on the successful   reassembly of IPv6 fragments in those cases where SEND packets do not   fit into the link Maximum Transmission Unit (MTU) (see [RFC6980]).8.7.  Cryptanalysis   A number of algorithms discussed in this document (such as those   described in Sections 7.4.2 and 7.4.3) rely on PRFs.  Implementations   that employ weak PRFs or keys of inappropriate size can be subject to   cryptanalysis, where an attacker can obtain the secret key employed   for the PRF, predict numeric identifiers, etc.   Furthermore, an implementation that overloads the semantics of the   secret key can result in more trivial cryptanalysis, possibly   resulting in the leakage of the value employed for the secret key.      |  NOTE: [IPID-DEV] describes two vulnerable transient numeric      |  identifier generators that employ cryptographically weak hash      |  functions.  Additionally, one of such implementations employs      |  32 bits of a kernel address as the secret key for a hash      |  function, and therefore, successful cryptanalysis leaks the      |  aforementioned kernel address, allowing for Kernel Address      |  Space Layout Randomization (KASLR) [KASLR] bypass.9.  Vulnerability Assessment of Transient Numeric Identifiers   The following subsections analyze possible vulnerabilities associated   with the algorithms described in Section 7.9.1.  Category #1: Uniqueness (Soft Failure)   Possible vulnerabilities associated with the algorithms from   Section 7.1 include the following:   *  use of flawed PRNGs (please see, e.g., [Zalewski2001],      [Zalewski2002], [Klein2007], and [CVEs])   *  inadvertently affecting the distribution of an otherwise suitable      PRNG (please see, e.g., [Romailler2020])   Where available, CSPRNGs should be preferred over regular PRNGs, such   as, e.g., POSIX random(3) implementations.  In scenarios where a   CSPRNG is not readily available, a security and privacy assessment of   employing a regular PRNG should be performed, supporting the   implementation decision.      |  NOTE: Please see [RFC4086] regarding randomness requirements      |  for security.  [Aumasson2018], [Press1992], and [Knuth1983]      |  discuss theoretical and practical aspects of random number      |  generation and provide guidance on how to evaluate PRNGs.   When employing a PRNG, many implementations "adapt" the length of its   output with a modulo operator (e.g., C language's "%"), possibly   changing the distribution of the output of the PRNG.   For example, consider an implementation that employs the following   code:              id = random() % 50000;   This example implementation means to obtain a transient numeric   identifier in the range 0-49999.  If random() produces, e.g., a   pseudorandom number of 16 bits (with uniform distribution), the   selected transient numeric identifier will have a nonuniform   distribution with the numbers in the range 0-15535 having double   frequency than the numbers in the range 15536-49999.      |  NOTE: For example, in our sample code, both an output of 10 and      |  output of 50010 from the random() function will result in an      |  "id" value of 10.   This effect is reduced if the PRNG produces an output that is much   longer than the length implied by the modulo operation.  We note that   to preserve a uniform distribution, the rejection sampling technique   [Romailler2020] can be used.   Use of algorithms other than PRNGs for generating identifiers of this   category is discouraged.9.2.  Category #2: Uniqueness (Hard Failure)   As noted in Section 7.2, this category can employ the same algorithms   as Category #4, since a monotonically increasing sequence tends to   minimize the transient numeric identifier reuse frequency.   Therefore, the vulnerability analysis in Section 9.4 also applies to   this category.   Additionally, as noted in Section 7.2, some transient numeric   identifiers of this category might be able to use the algorithms from   Section 7.1, in which case the same considerations as in Section 9.1   would apply.9.3.  Category #3: Uniqueness, Stable within Context (Soft Failure)   Possible vulnerabilities associated with the algorithms from   Section 7.3 are the following:   *  Use of weak PRFs or inappropriate secret keys (whether      inappropriate selection or inappropriate size) could allow for      cryptanalysis, which could eventually be exploited by an attacker      to predict future transient numeric identifiers.   *  Since the algorithm generates a unique and stable identifier      within a specified context, it may allow for network activity      correlation and fingerprinting within the specified context.9.4.  Category #4: Uniqueness, Monotonically Increasing within Context      (Hard Failure)   The algorithm described in Section 7.4.1 for generating identifiers   of Category #4 will result in an identifiable pattern (i.e., a   monotonically increasing sequence) for the transient numeric   identifiers generated for each CONTEXT, and thus will allow for   fingerprinting and network activity correlation within each CONTEXT.   On the other hand, a simple way to generalize and analyze the   algorithms described in Sections 7.4.2 and 7.4.3 for generating   identifiers of Category #4 is as follows:       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       retry = id_range;       id_inc = increment() % id_range;       do {           update_mono(CONTEXT, id_inc);           next_id = min_id + (offset(CONTEXT) + \                               mono(CONTEXT)) % id_range;           if (suitable_id(next_id)) {               return next_id;           }           retry = retry - id_inc;       } while (retry > 0);       return ERROR;   NOTE:      increment() returns a small integer that is employed to generate a      monotonically increasing function.  Most implementations employ a      constant value for "increment()" (usually 1).  The value returned      by increment() must be much smaller than the value computed for      "id_range".      update_mono(CONTEXT, id_inc) increments the counter corresponding      to CONTEXT by "id_inc".      mono(CONTEXT) reads the counter corresponding to CONTEXT.   Essentially, an identifier (next_id) is generated by adding a   monotonically increasing function (mono()) to an offset value, which   is unknown to the attacker and stable for given context (CONTEXT).   The following aspects of the algorithm should be considered:   *  For the most part, it is the offset() function that results in      identifiers that are unpredictable by an off-patch attacker.      While the resulting sequence is known to be monotonically      increasing, the use of a randomized offset value makes the      resulting values unknown to the attacker.   *  The most straightforward "stateless" implementation of offset() is      with a PRF that takes the values that identify the context and a      secret key (not shown in the figure above) as arguments.   *  One possible implementation of mono() would be to have mono()      internally employ a single counter (as in the algorithm from      Section 7.4.2) or map the increments for different contexts into a      number of counters/buckets, such that the number of counters that      need to be maintained in memory is reduced (as in the "Double-PRF      Algorithm" from Section 7.4.3).   *  In all cases, a monotonically increasing function is implemented      by incrementing the previous value of a counter by increment()      units.  In the most trivial case, increment() could return the      constant "1".  But increment() could also be implemented to return      small random integers such that the increments are unpredictable      (see Appendix A.2 of this document).  This represents a trade-off      between the unpredictability of the resulting transient numeric      identifiers and the transient numeric identifier reuse frequency.   Considering the generic algorithm illustrated above, we can identify   the following possible vulnerabilities:   *  Since the algorithms for this category are similar to those of      Section 9.3, with the addition of a monotonically increasing      function, all the issues discussed in Section 9.3 ("Category #3:      Uniqueness, Stable within Context (Soft Failure)") also apply to      this case.   *  mono() can be correlated to the number of identifiers generated      for a given context (CONTEXT).  Thus, if mono() spans more than      the necessary context, the "increments" could be leaked to other      parties, thus disclosing information about the number of      identifiers that have been generated by the algorithm for all      contexts.  This information disclosure becomes more evident when      an implementation employs a constant increment of 1.  For example,      an implementation where mono() is actually a single global counter      will unnecessarily leak information about the number of      identifiers that have been generated by the algorithm (globally,      for all contexts).  [Fyodor2003] describes one example of how such      information leakages can be exploited.  We note that limiting the      span of the increment space will require a larger number of      counters to be stored in memory (i.e., a larger value for the      TABLE_LENGTH parameter of the algorithm in Section 7.4.3).   *  Transient numeric identifiers generated with the algorithms      described in Sections 7.4.2 and 7.4.3 will normally allow for      fingerprinting within CONTEXT since, for such context, the      resulting identifiers will have an identifiable pattern (i.e., a      monotonically increasing sequence).10.  IANA Considerations   This document has no IANA actions.11.  Security Considerations   This entire document is about the security and privacy implications   of transient numeric identifiers.  [RFC9416] recommends that protocol   specifications specify the interoperability requirements of their   transient numeric identifiers, perform a vulnerability assessment of   their transient numeric identifiers, and recommend an algorithm for   generating each of their transient numeric identifiers.  This   document analyzes possible algorithms (and their implications) that   could be employed to comply with the interoperability requirements of   the most common categories of transient numeric identifiers while   minimizing the associated negative security and privacy implications.12.  References12.1.  Normative References   [RFC0791]  Postel, J., "Internet Protocol", STD 5, RFC 791,              DOI 10.17487/RFC0791, September 1981,              <https://www.rfc-editor.org/info/rfc791>.   [RFC0793]  Postel, J., "Transmission Control Protocol", RFC 793,              DOI 10.17487/RFC0793, September 1981,              <https://www.rfc-editor.org/info/rfc793>.   [RFC1035]  Mockapetris, P., "Domain names - implementation and              specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,              November 1987, <https://www.rfc-editor.org/info/rfc1035>.   [RFC1321]  Rivest, R., "The MD5 Message-Digest Algorithm", RFC 1321,              DOI 10.17487/RFC1321, April 1992,              <https://www.rfc-editor.org/info/rfc1321>.   [RFC2460]  Deering, S. and R. Hinden, "Internet Protocol, Version 6              (IPv6) Specification", RFC 2460, DOI 10.17487/RFC2460,              December 1998, <https://www.rfc-editor.org/info/rfc2460>.   [RFC4086]  Eastlake 3rd, D., Schiller, J., and S. Crocker,              "Randomness Requirements for Security", BCP 106, RFC 4086,              DOI 10.17487/RFC4086, June 2005,              <https://www.rfc-editor.org/info/rfc4086>.   [RFC4291]  Hinden, R. and S. Deering, "IP Version 6 Addressing              Architecture", RFC 4291, DOI 10.17487/RFC4291, February              2006, <https://www.rfc-editor.org/info/rfc4291>.   [RFC4862]  Thomson, S., Narten, T., and T. Jinmei, "IPv6 Stateless              Address Autoconfiguration", RFC 4862,              DOI 10.17487/RFC4862, September 2007,              <https://www.rfc-editor.org/info/rfc4862>.   [RFC5722]  Krishnan, S., "Handling of Overlapping IPv6 Fragments",              RFC 5722, DOI 10.17487/RFC5722, December 2009,              <https://www.rfc-editor.org/info/rfc5722>.   [RFC5905]  Mills, D., Martin, J., Ed., Burbank, J., and W. Kasch,              "Network Time Protocol Version 4: Protocol and Algorithms              Specification", RFC 5905, DOI 10.17487/RFC5905, June 2010,              <https://www.rfc-editor.org/info/rfc5905>.   [RFC5925]  Touch, J., Mankin, A., and R. Bonica, "The TCP              Authentication Option", RFC 5925, DOI 10.17487/RFC5925,              June 2010, <https://www.rfc-editor.org/info/rfc5925>.   [RFC6056]  Larsen, M. and F. Gont, "Recommendations for Transport-              Protocol Port Randomization", BCP 156, RFC 6056,              DOI 10.17487/RFC6056, January 2011,              <https://www.rfc-editor.org/info/rfc6056>.   [RFC6151]  Turner, S. and L. Chen, "Updated Security Considerations              for the MD5 Message-Digest and the HMAC-MD5 Algorithms",              RFC 6151, DOI 10.17487/RFC6151, March 2011,              <https://www.rfc-editor.org/info/rfc6151>.   [RFC6191]  Gont, F., "Reducing the TIME-WAIT State Using TCP              Timestamps", BCP 159, RFC 6191, DOI 10.17487/RFC6191,              April 2011, <https://www.rfc-editor.org/info/rfc6191>.   [RFC6437]  Amante, S., Carpenter, B., Jiang, S., and J. Rajahalme,              "IPv6 Flow Label Specification", RFC 6437,              DOI 10.17487/RFC6437, November 2011,              <https://www.rfc-editor.org/info/rfc6437>.   [RFC6528]  Gont, F. and S. Bellovin, "Defending against Sequence              Number Attacks", RFC 6528, DOI 10.17487/RFC6528, February              2012, <https://www.rfc-editor.org/info/rfc6528>.   [RFC7217]  Gont, F., "A Method for Generating Semantically Opaque              Interface Identifiers with IPv6 Stateless Address              Autoconfiguration (SLAAC)", RFC 7217,              DOI 10.17487/RFC7217, April 2014,              <https://www.rfc-editor.org/info/rfc7217>.   [RFC7323]  Borman, D., Braden, B., Jacobson, V., and R.              Scheffenegger, Ed., "TCP Extensions for High Performance",              RFC 7323, DOI 10.17487/RFC7323, September 2014,              <https://www.rfc-editor.org/info/rfc7323>.   [RFC8064]  Gont, F., Cooper, A., Thaler, D., and W. Liu,              "Recommendation on Stable IPv6 Interface Identifiers",              RFC 8064, DOI 10.17487/RFC8064, February 2017,              <https://www.rfc-editor.org/info/rfc8064>.   [RFC8200]  Deering, S. and R. Hinden, "Internet Protocol, Version 6              (IPv6) Specification", STD 86, RFC 8200,              DOI 10.17487/RFC8200, July 2017,              <https://www.rfc-editor.org/info/rfc8200>.   [RFC8981]  Gont, F., Krishnan, S., Narten, T., and R. Draves,              "Temporary Address Extensions for Stateless Address              Autoconfiguration in IPv6", RFC 8981,              DOI 10.17487/RFC8981, February 2021,              <https://www.rfc-editor.org/info/rfc8981>.   [RFC9293]  Eddy, W., Ed., "Transmission Control Protocol (TCP)",              STD 7, RFC 9293, DOI 10.17487/RFC9293, August 2022,              <https://www.rfc-editor.org/info/rfc9293>.12.2.  Informative References   [ARC4RANDOM]              OpenBSD, "arc4random(3)", Library Functions Manual,              September 2019, <https://man.openbsd.org/arc4random>.   [Aumasson2018]              Aumasson, J-P., "Serious Cryptography: A Practical              Introduction to Modern Encryption", No Starch Press, Inc.,              ISBN-10 1-59327-826-8, November 2017.   [Bellovin1989]              Bellovin, S., "Security Problems in the TCP/IP Protocol              Suite", Computer Communications Review, Vol. 19, No. 2,              pp. 32-48, April 1989,              <https://www.cs.columbia.edu/~smb/papers/ipext.pdf>.   [Bellovin2002]              Bellovin, S., "A Technique for Counting NATted Hosts",              IMW'02, Marseille, France, ISBN 1-58113-603-X/02/0011,              November 2002,              <https://www.cs.columbia.edu/~smb/papers/fnat.pdf>.   [BLAKE3]   "BLAKE3: one function, fast everywhere", September 2022,              <https://blake3.io/>.   [C11]      ISO/IEC, "Information technology - Programming languages -              C", ISO/IEC 9899:2018, June 2018.   [CPNI-TCP] Centre for the Protection of National Infrastructure              (CPNI), "Security Assessment of the Transmission Control              Protocol (TCP)", CPNI Technical Note 3/2009, February              2009, <https://www.si6networks.com/files/publications/tn-              03-09-security-assessment-TCP.pdf>.   [CVEs]     NVD, "Vulnerability Advisories for PRNGs",              <https://www.gont.com.ar/miscellanea/prng-cves/>.   [EFF]      EFF, "Cover your tracks: See how trackers view your              browser", <https://coveryourtracks.eff.org/>.   [FIPS-SHS] NIST, "Secure Hash Standard (SHS)", FIPS PUB 180-4,              DOI 10.6028/NIST.FIPS.180-4, August 2015,              <https://nvlpubs.nist.gov/nistpubs/FIPS/              NIST.FIPS.180-4.pdf>.   [Fyodor1998]              Fyodor, "Remote OS detection via TCP/IP Stack              FingerPrinting", Phrack Magazine, Volume 8, Issue 54,              December 1998,              <http://www.phrack.org/archives/issues/54/9.txt>.   [Fyodor2003]              Fyodor, "Idle Scanning and related IPID games", 2003,              <https://nmap.org/presentations/CanSecWest03/CD_Content/              idlescan_paper/idlescan.html>.   [Fyodor2006]              Lyon, G., "Chapter 8. Remote OS Detection", January 2009,              <https://nmap.org/book/osdetect.html>.   [GETENTROPY]              Linux, "getentropy(3)", Linux Programmer's Manual, March              2021,              <https://man7.org/linux/man-pages/man3/getentropy.3.html>.   [IANA-PROT]              IANA, "Protocol Registries",              <https://www.iana.org/protocols>.   [IPID-DEV] Klein, A. and B. Pinkas, "From IP ID to Device ID and              KASLR Bypass (Extended Version)",              DOI 10.48550/arXiv.1906.10478, October 2019,              <https://arxiv.org/pdf/1906.10478.pdf>.   [Joncheray1995]              Joncheray, L., "Simple Active Attack Against TCP",              Proceedings of the Fifth USENIX UNIX Security Symposium,              June 1995, <https://www.usenix.org/legacy/publications/lib              rary/proceedings/security95/full_papers/joncheray.pdf>.   [KASLR]    PaX Team, "Address Space Layout Randomization",              <https://pax.grsecurity.net/docs/aslr.txt>.   [Klein2007]              Klein, A., "OpenBSD DNS Cache Poisoning and Multiple O/S              Predictable IP ID Vulnerability", November 2007,              <https://dl.packetstormsecurity.net/papers/attack/OpenBSD_              DNS_Cache_Poisoning_and_Multiple_OS_Predictable_IP_ID_Vuln              erability.pdf>.   [Knuth1983]              Knuth, D., "The Art of Computer Programming", Volume 2              (Seminumerical Algorithms), 2nd Ed., Reading,              Massachusetts, Addison-Wesley Publishing Company, January              1981.   [Morris1985]              Morris, R., "A Weakness in the 4.2BSD UNIX TCP/IP              Software", CSTR 117, AT&T Bell Laboratories, Murray Hill,              NJ, February 1985,              <https://pdos.csail.mit.edu/~rtm/papers/117.pdf>.   [nmap]     nmap, "Nmap: Free Security Scanner For Network Exploration              and Audit", 2020, <https://nmap.org/>.   [POSIX]    IEEE, "IEEE Standard for Information Technology --              Portable Operating System Interface (POSIX(TM)) Base              Specifications, Issue 7", IEEE Std 1003.1-2017,              DOI 10.1109/IEEESTD.2018.8277153, January 2018,              <https://doi.org/10.1109/IEEESTD.2018.8277153>.   [Press1992]              Press, W., Teukolsky, S., Vetterling, W., and B. Flannery,              "Numerical Recipes in C: The Art of Scientific Computing",              2nd Ed., Cambridge University Press, ISBN 0-521-43108-5,              December 1992.   [RFC2104]  Krawczyk, H., Bellare, M., and R. Canetti, "HMAC: Keyed-              Hashing for Message Authentication", RFC 2104,              DOI 10.17487/RFC2104, February 1997,              <https://www.rfc-editor.org/info/rfc2104>.   [RFC3971]  Arkko, J., Ed., Kempf, J., Zill, B., and P. Nikander,              "SEcure Neighbor Discovery (SEND)", RFC 3971,              DOI 10.17487/RFC3971, March 2005,              <https://www.rfc-editor.org/info/rfc3971>.   [RFC4953]  Touch, J., "Defending TCP Against Spoofing Attacks",              RFC 4953, DOI 10.17487/RFC4953, July 2007,              <https://www.rfc-editor.org/info/rfc4953>.   [RFC4963]  Heffner, J., Mathis, M., and B. Chandler, "IPv4 Reassembly              Errors at High Data Rates", RFC 4963,              DOI 10.17487/RFC4963, July 2007,              <https://www.rfc-editor.org/info/rfc4963>.   [RFC5927]  Gont, F., "ICMP Attacks against TCP", RFC 5927,              DOI 10.17487/RFC5927, July 2010,              <https://www.rfc-editor.org/info/rfc5927>.   [RFC6194]  Polk, T., Chen, L., Turner, S., and P. Hoffman, "Security              Considerations for the SHA-0 and SHA-1 Message-Digest              Algorithms", RFC 6194, DOI 10.17487/RFC6194, March 2011,              <https://www.rfc-editor.org/info/rfc6194>.   [RFC6274]  Gont, F., "Security Assessment of the Internet Protocol              Version 4", RFC 6274, DOI 10.17487/RFC6274, July 2011,              <https://www.rfc-editor.org/info/rfc6274>.   [RFC6973]  Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,              Morris, J., Hansen, M., and R. Smith, "Privacy              Considerations for Internet Protocols", RFC 6973,              DOI 10.17487/RFC6973, July 2013,              <https://www.rfc-editor.org/info/rfc6973>.   [RFC6980]  Gont, F., "Security Implications of IPv6 Fragmentation              with IPv6 Neighbor Discovery", RFC 6980,              DOI 10.17487/RFC6980, August 2013,              <https://www.rfc-editor.org/info/rfc6980>.   [RFC7098]  Carpenter, B., Jiang, S., and W. Tarreau, "Using the IPv6              Flow Label for Load Balancing in Server Farms", RFC 7098,              DOI 10.17487/RFC7098, January 2014,              <https://www.rfc-editor.org/info/rfc7098>.   [RFC7258]  Farrell, S. and H. Tschofenig, "Pervasive Monitoring Is an              Attack", BCP 188, RFC 7258, DOI 10.17487/RFC7258, May              2014, <https://www.rfc-editor.org/info/rfc7258>.   [RFC7707]  Gont, F. and T. Chown, "Network Reconnaissance in IPv6              Networks", RFC 7707, DOI 10.17487/RFC7707, March 2016,              <https://www.rfc-editor.org/info/rfc7707>.   [RFC7721]  Cooper, A., Gont, F., and D. Thaler, "Security and Privacy              Considerations for IPv6 Address Generation Mechanisms",              RFC 7721, DOI 10.17487/RFC7721, March 2016,              <https://www.rfc-editor.org/info/rfc7721>.   [RFC7739]  Gont, F., "Security Implications of Predictable Fragment              Identification Values", RFC 7739, DOI 10.17487/RFC7739,              February 2016, <https://www.rfc-editor.org/info/rfc7739>.   [RFC8446]  Rescorla, E., "The Transport Layer Security (TLS) Protocol              Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,              <https://www.rfc-editor.org/info/rfc8446>.   [RFC8937]  Cremers, C., Garratt, L., Smyshlyaev, S., Sullivan, N.,              and C. Wood, "Randomness Improvements for Security              Protocols", RFC 8937, DOI 10.17487/RFC8937, October 2020,              <https://www.rfc-editor.org/info/rfc8937>.   [RFC9414]  Gont, F. and I. Arce, "Unfortunate History of Transient              Numeric Identifiers", RFC 9414, DOI 10.17487/RFC9414, July              2023, <https://www.rfc-editor.org/info/rfc9414>.   [RFC9416]  Gont, F. and I. Arce, "Security Considerations for              Transient Numeric Identifiers Employed in Network              Protocols", BCP 72, RFC 9416, DOI 10.17487/RFC9416, July              2023, <https://www.rfc-editor.org/info/rfc9416>.   [Romailler2020]              Romailler, Y., "The Definitive Guide to "Modulo Bias and              How to Avoid It"!", Kudelski Security Research, July 2020,              <https://research.kudelskisecurity.com/2020/07/28/the-              definitive-guide-to-modulo-bias-and-how-to-avoid-it/>.   [Sanfilippo1998a]              Sanfilippo, S., "about the ip header id", message to the              Bugtraq mailing list, December 1998,              <http://seclists.org/bugtraq/1998/Dec/48>.   [Sanfilippo1998b]              Sanfilippo, S., "new tcp scan method", message to the              Bugtraq mailing list, 18 December 1998,              <https://seclists.org/bugtraq/1998/Dec/79>.   [Sanfilippo1999]              Sanfilippo, S., "more ip id", message to the Bugtraq              mailing list, November 1999,              <https://github.com/antirez/hping/raw/master/docs/MORE-              FUN-WITH-IPID>.   [Schuba1993]              Schuba, C., "Addressing Weakness in the Domain Name System              Protocol", August 1993,              <http://ftp.cerias.purdue.edu/pub/papers/christoph-schuba/              schuba-DNS-msthesis.pdf>.   [Shimomura1995]              Shimomura, T., "Technical details of the attack described              by Markoff in NYT", message to the USENET              comp.security.misc newsgroup, 25 January 1995,              <https://www.gont.com.ar/files/post-shimomura-usenet.txt>.   [Silbersack2005]              Silbersack, M., "Improving TCP/IP security through              randomization without sacrificing interoperability",              EuroBSDCon 2005 Conference,              <https://www.silby.com/eurobsdcon05/              eurobsdcon_silbersack.pdf>.   [SipHash]  "SipHash: a fast short-input PRF", February 2023,              <https://github.com/veorq/SipHash>.   [TBIT]     TBIT, "TBIT, the TCP Behavior Inference Tool", 2001,              <https://www.icir.org/tbit/>.   [TCPT-uptime]              McDanel, B., "TCP Timestamping - Obtaining System Uptime              Remotely", message to the Bugtraq mailing list, March              2001, <https://seclists.org/bugtraq/2001/Mar/182>.   [Zalewski2001]              Zalewski, M., "Strange Attractors and TCP/IP Sequence              Number Analysis", April 2001,              <https://lcamtuf.coredump.cx/oldtcp/tcpseq.html>.   [Zalewski2002]              Zalewski, M., "Strange Attractors and TCP/IP Sequence              Number Analysis - One Year Later (2002)",              <https://lcamtuf.coredump.cx/newtcp/>.   [Zalewski2012]              Zalewski, M., "p0f v3 (3.09b)",              <https://lcamtuf.coredump.cx/p0f.shtml>.Appendix A.  Algorithms and Techniques with Known Issues   The following subsections discuss algorithms and techniques with   known negative security and privacy implications.      |  NOTE: As discussed in Section 1, the use of cryptographic      |  techniques might allow for the safe use of some of these      |  algorithms and techniques.  However, this should be evaluated      |  on a case-by-case basis.A.1.  Predictable Linear Identifiers Algorithm   One of the most trivial ways to achieve uniqueness with a low   identifier reuse frequency is to produce a linear sequence.  This   type of algorithm has been employed in the past to generate   identifiers of Categories #1, #2, and #4 (please see Section 6 for an   analysis of these categories).   For example, the following algorithm has been employed (see, e.g.,   [Morris1985], [Shimomura1995], [Silbersack2005], and [CPNI-TCP]) in a   number of operating systems for selecting IP IDs, TCP ephemeral port   numbers, etc.:       /* Initialization code */       next_id = min_id;       id_inc= 1;       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       retry = id_range;       do {           if (next_id == max_id) {               next_id = min_id;           }           else {               next_id = next_id + id_inc;           }           if (suitable_id(next_id)) {               return next_id;           }           retry--;       } while (retry > 0);       return ERROR;   NOTE:      suitable_id() checks whether a candidate numeric identifier is      suitable (e.g., whether it is unique or not).   For obvious reasons, this algorithm results in predictable sequences.   Since a global counter is used to generate the transient numeric   identifiers ("next_id" in the example above), an entity that learns   one numeric identifier can infer past numeric identifiers and predict   future values to be generated by the same algorithm.  Since the value   employed for the increments is known (such as "1" in this case), an   attacker can sample two values and learn the number of identifiers   that were generated in between the two sampled values.  Furthermore,   if the counter is initialized, to some known value (e.g., when the   system is bootstrapped), the algorithm will leak additional   information, such as the number of transmitted fragmented datagrams   in the case of an IP ID generator [Sanfilippo1998a] or the system   uptime in the case of TCP timestamps [TCPT-uptime].A.2.  Random-Increments Algorithm   This algorithm offers a middle ground between the algorithms that   generate randomized transient numeric identifiers (such as those   described in Sections 7.1.1 and 7.1.2) and those that generate   identifiers with a predictable monotonically increasing function (see   Appendix A.1).       /* Initialization code */       next_id = random();        /* Initialization value */       id_rinc = 500;             /* Determines the trade-off */       /* Transient Numeric ID selection function */       id_range = max_id - min_id + 1;       retry = id_range;       do {           /* Random increment */           id_inc = (random() % id_rinc) + 1;           if ( (max_id - next_id) >= id_inc){               next_id = next_id + id_inc;           }           else {               next_id = min_id + id_inc - (max_id - next_id);           }           if (suitable_id(next_id)) {              return next_id;           }           retry = retry - id_inc;       } while (retry > 0);       return ERROR;   NOTE:      random() is a PRNG that returns a pseudorandom unsigned integer      number of appropriate size.  Beware that "adapting" the length of      the output of random() with a modulo operator (e.g., C language's      "%") may change the distribution of the PRNG.  To preserve a      uniform distribution, the rejection sampling technique      [Romailler2020] can be used.      suitable_id() is a function that checks whether a candidate      identifier is suitable (e.g., whether it is unique or not).   This algorithm aims at producing a global monotonically increasing   sequence of transient numeric identifiers while avoiding the use of   fixed increments, which would lead to trivially predictable   sequences.  The value "id_rinc" allows for direct control of the   trade-off between unpredictability and identifier reuse frequency.   The smaller the value of "id_rinc", the more similar this algorithm   is to a predicable, global linear identifier generation algorithm (as   the one in Appendix A.1).  The larger the value of "id_rinc", the   more similar this algorithm is to the algorithm described in   Section 7.1.1 of this document.   When the identifiers wrap, there is a risk of collisions of transient   numeric identifiers (i.e., identifier reuse).  Therefore, "id_rinc"   should be selected according to the following criteria:   *  It should maximize the wrapping time of the identifier space.   *  It should minimize identifier reuse frequency.   *  It should maximize unpredictability.   Clearly, these are competing goals, and the decision of which value   of "id_rinc" to use is a trade-off.  Therefore, the value of   "id_rinc" is at times a configurable parameter so that system   administrators can make the trade-off for themselves.  We note that   the alternative algorithms discussed throughout this document offer   better interoperability, security, and privacy properties than this   algorithm, and hence, implementation of this algorithm is   discouraged.A.3.  Reusing Identifiers Across Different Contexts   Employing the same identifier across contexts in which stability is   not required (i.e., overloading the semantics of transient numeric   identifiers) usually has negative security and privacy implications.   For example, in order to generate transient numeric identifiers of   Category #2 or #3, an implementation or specification might be   tempted to employ a source for the numeric identifiers that is known   to provide unique values but that may also be predictable or leak   information related to the entity generating the identifier.  This   technique has been employed in the past for, e.g., generating IPv6   IIDs by reusing the MAC address of the underlying network interface   card.  However, as noted in [RFC7721] and [RFC7707], embedding link-   layer addresses in IPv6 IIDs not only results in predictable values   but also leaks information about the manufacturer of the underlying   network interface card, allows for network activity correlation, and   makes address-based scanning attacks feasible.Acknowledgements   The authors would like to thank (in alphabetical order) Bernard   Aboba, Jean-Philippe Aumasson, Steven Bellovin, Luis León Cárdenas   Graide, Spencer Dawkins, Theo de Raadt, Guillermo Gont, Joseph   Lorenzo Hall, Gre Norcie, Colin Perkins, Vincent Roca, Shivan Sahib,   Rich Salz, Martin Thomson, and Michael Tüxen for providing valuable   comments on earlier draft versions of this document.   The authors would like to thank Shivan Sahib and Christopher Wood for   their guidance during the publication process of this document.   The authors would like to thank Jean-Philippe Aumasson and Mathew   D. Green (John Hopkins University) for kindly answering a number of   questions.   The authors would like to thank Diego Armando Maradona for his magic   and inspiration.Authors' Addresses   Fernando Gont   SI6 Networks   Segurola y Habana 4310 7mo piso   Ciudad Autonoma de Buenos Aires   Argentina   Email: fgont@si6networks.com   URI:   https://www.si6networks.com   Ivan Arce   Quarkslab   Segurola y Habana 4310 7mo piso   Ciudad Autonoma de Buenos Aires   Argentina   Email: iarce@quarkslab.com   URI:   https://www.quarkslab.com