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Plonky3 MultiField32Challenger: transcript malleability and challenge entropy loss

High severity GitHub Reviewed Published May 15, 2026 in Plonky3/Plonky3 • Updated Jun 11, 2026

Package

cargo p3-challenger (Rust)

Affected versions

< 0.4.3
>= 0.5.0, < 0.5.3

Patched versions

0.4.3
0.5.3

Description

Impact

  • Key: challenger/src/multi_field_challenger.rs | MultiField32Challenger::duplexing | transcript_malleability

  • Affected files: challenger/src/multi_field_challenger.rs, field/src/helpers.rs

  • Violated invariant: The Fiat-Shamir sponge must bind challenges to the exact sequence of observed field elements. Specifically: (1) absorption must be injective — distinct observation streams must produce distinct sponge states, (2) squeezing must be injective — distinct PF rate cells must yield distinct F challenge sequences, and (3) all bits of each absorbed PF element must influence the sponge state.

  • Exploit scenario: An attacker controlling prover-side observations can craft distinct transcripts that produce identical challenges, breaking the binding property of Fiat-Shamir. Three independent attack vectors exist:

    1. Partial-chunk aliasing (absorb): duplexing() packs input_buffer.chunks(num_f_elms) via reduce_32 (base 2^32) with no length marker and no zeroing of unused rate slots. Observing [x] followed by a sample yields the same sponge state as [x, 0, ..., 0] (padded to num_f_elms) followed by a sample, since reduce_32 treats missing high limbs identically to explicit zeros. The attacker can extend or truncate the tail of any observation batch without changing future challenges.

    2. Non-injective squeeze (squeeze): split_32 decomposes each PF rate cell into base-2^64 digits and maps each through TF::from_u64, which reduces mod F::ORDER (~2^31). Two distinct PF values whose base-2^64 digits differ only in their upper 33 bits produce identical F challenge sequences. This weakens the entropy of sampled challenges and can enable selective forgery when the attacker can influence the sponge state pre-squeeze.

    3. High-bit truncation (observe Hash/MerkleCap): num_f_elms = PF::bits() / 64 computes the number of F limbs per PF element. For BN254 (254-bit field), this yields 3 limbs covering 192 bits — the top 62 bits of every digest word are silently discarded. An attacker can find two distinct BN254 hash digests that differ only in bits 192–253 and observe them interchangeably without affecting challenges.

  • Evidence: In duplexing(), the absorb path (reduce_32 with base 2^32) and the squeeze path (split_32 with base 2^64) use incompatible radices with no length domain separation. reduce_32 is a plain Horner fold acc * 2^32 + digit with no padding or tag, so trailing zeros are free. split_32 extracts u64 digits and casts each via TF::from_u64, which performs modular reduction, collapsing the top bits. The limb count PF::bits() / 64 is a floor division that silently drops all bits beyond 64 * num_f_elms for fields whose bit-width is not a multiple of 64.

Patches

Included in v0.4.3 and v0.5.3

References

@Nashtare Nashtare published to Plonky3/Plonky3 May 15, 2026
Published to the GitHub Advisory Database May 21, 2026
Reviewed May 21, 2026
Published by the National Vulnerability Database Jun 10, 2026
Last updated Jun 11, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity High
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity High
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:N/VI:H/VA:N/SC:N/SI:H/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(2nd percentile)

Weaknesses

Insufficient Verification of Data Authenticity

The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data. Learn more on MITRE.

Use of a Cryptographic Primitive with a Risky Implementation

To fulfill the need for a cryptographic primitive, the product implements a cryptographic algorithm using a non-standard, unproven, or disallowed/non-compliant cryptographic implementation. Learn more on MITRE.

CVE ID

CVE-2026-46654

GHSA ID

GHSA-vj64-rjf3-w3v7

Source code

Credits

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