DavidOsipov PostQuantum-Feldman-VSS: Observable Discrepancy (CVE-2025-29780)
Published Sources: CVE.org, OSV
What is affected, what to do
Post-Quantum Secure Feldman's Verifiable Secret Sharing provides a Python implementation of Feldman's Verifiable Secret Sharing (VSS) scheme. In versions 0.8.0b2 and prior, the `feldman_vss` library contains timing side-channel vulnerabilities in its matrix operations, specifically within the `_find_secure_pivot` function and potentially other parts of `_secure_matrix_solve`.
The available sources name no fixed version. Check the vendor guidance in the references below.
- Affected
- DavidOsipov PostQuantum-Feldman-VSS
lte *..0.8.0b2
- DavidOsipov PostQuantum-Feldman-VSS
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Metrics
5.8
Priority
XIEM Effective CVSS: highest available CVSS rating from NVD and vendor data.
Source: NVD, CVSS v4
5.8
CVSS v4
Severity Medium on the scale from 0 to 10.
Source: CVE.org, OSV
0.2 %
EPSS score
Low — model estimates < 1% exploitation likelihood.
Source: FIRST EPSS, percentile 13.1
Show all metrics
- Severity
- Medium
Priority (0–325)
The priority score is a consolidated metric that condenses several individual signals into a single, action-guiding ranking: technical severity (CVSS), exploitation likelihood (EPSS), known-exploited status (KEV) and the context of the affected asset. The aim is to answer the only truly important question in vulnerability management: what first?
Read in glossary- 38.66 Methodology
Exploit maturity
An exploit is code or a technique that specifically uses a concrete vulnerability to force unintended behaviour — such as executing code or escalating privileges. It is the tool that turns a theoretical flaw into a practical attack.
Read in glossary- no public PoC known
XIEM Effective CVSS
XIEM® is the tiered managed-security operating model from NEOSEC and a registered trademark of NEOSEC GmbH. It extends classic SIEM — the central collection, normalisation and correlation of security-relevant events — with endpoint forensics, network visibility and deception technology, and consolidates them into three building tiers: Sentry, Orchestrate and Command.
Read in glossary- 5.8 Methodology
CVSS v4
CVSS is the globally established, open standard for assessing the technical severity of a vulnerability on a scale from 0 to 10. It describes how easily a flaw can be exploited and what damage it can cause — but not how likely its actual exploitation is. CVSS and the complementary EPSS deliberately answer different questions.
Read in glossary- 5.8
EPSS score
The EPSS score is the absolute model output for an individual vulnerability: the estimated probability of real-world exploitation within the next 30 days, expressed as a value between 0 and 1 (i.e. 0 % to 100 %). A score of 0.42 means „the model estimates a 42 % chance of real-world exploitation within 30 days“.
Read in glossary- 0.2 %
- Low — model estimates < 1% exploitation likelihood.
EPSS percentile
The EPSS percentile is the relative position of a vulnerability within all EPSS-scored CVEs, expressed as a value between 0 and 1. A percentile of 0.90 means „this CVE has a higher EPSS score than 90 % of all other scored CVEs“. Unlike the EPSS score, it is not a probability — but a positioning within a ranking.
Read in glossary- 13.1
- Elevated — this CVE ranks above at least 10% of all CVEs scored today.
KEV status
The KEV catalogue is a list maintained by the US authority CISA of vulnerabilities for which active exploitation in the wild has been confirmed. It answers not the question „how severe?“ but „is it already being attacked?“ — thereby sharply prioritising patching.
Read in glossary- —
- Published
CWE
CWE is a catalogue of typical weakness types in software and hardware — such as flawed input validation, unsafe memory handling or poor authentication. Where a CVE names a concrete vulnerability in a concrete product, a CWE describes the underlying class of defect.
Read in glossary- CWE-203, CWE-208, CWE-385
Detection rules
No NEOSEC detection rule is available for this vulnerability yet.
Classification under NIS2 and CRA
Rule-based classification from exploitation status and severity. It does not replace a case-by-case assessment.
NIS2
NIS2 is EU Directive (EU) 2022/2555 to raise the level of cybersecurity across the Union. It substantially broadens obligations compared with the first NIS directive: more sectors, mandatory risk management, staged reporting duties and personal accountability of senior management.
Read in glossary- Handle the vulnerability in regular vulnerability handling under Art. 21(2)(e) NIS2.
CRA
The CRA is EU Regulation (EU) 2024/2847 setting horizontal cybersecurity requirements for „products with digital elements“ — from industrial controllers and software to connected consumer devices. For the first time manufacturers must ensure security across the entire product lifecycle, handle vulnerabilities and provide security updates.
Read in glossary- From 11.12.2027, Annex I Part II CRA requires manufacturers of affected products with digital elements to handle vulnerabilities without delay and provide security updates.
ISO/IEC 27001
ISO/IEC 27001 is the internationally leading standard for an information security management system (ISMS). It describes a risk-based, process-oriented framework to systematically plan, implement, review and improve information security.
Read in glossary- Control A.8.8, management of technical vulnerabilities: assess relevance to your systems, plan remediation and keep evidence.
Description
Post-Quantum Secure Feldman's Verifiable Secret Sharing provides a Python implementation of Feldman's Verifiable Secret Sharing (VSS) scheme. In versions 0.8.0b2 and prior, the `feldman_vss` library contains timing side-channel vulnerabilities in its matrix operations, specifically within the `_find_secure_pivot` function and potentially other parts of `_secure_matrix_solve`. These vulnerabilities are due to Python's execution model, which does not guarantee constant-time execution. An attacker with the ability to measure the execution time of these functions (e.g., through repeated calls with carefully crafted inputs) could potentially recover secret information used in the Verifiable Secret Sharing (VSS) scheme. The `_find_secure_pivot` function, used during Gaussian elimination in `_secure_matrix_solve`, attempts to find a non-zero pivot element. However, the conditional statement `if matrix[row][col] != 0 and row_random < min_value:` has execution time that depends on the value of `matrix[row][col]`. This timing difference can be exploited by an attacker. The `constant_time_compare` function in this file also does not provide a constant-time guarantee. The Python implementation of matrix operations in the _find_secure_pivot and _secure_matrix_solve functions cannot guarantee constant-time execution, potentially leaking information about secret polynomial coefficients. An attacker with the ability to make precise timing measurements of these operations could potentially extract secret information through statistical analysis of execution times, though practical exploitation would require significant expertise and controlled execution environments. Successful exploitation of these timing side-channels could allow an attacker to recover secret keys or other sensitive information protected by the VSS scheme. This could lead to a complete compromise of the shared secret. As of time of publication, no patched versions of Post-Quantum Secure Feldman's Verifiable Secret Sharing exist, but other mitigations are available. As acknowledged in the library's documentation, these vulnerabilities cannot be adequately addressed in pure Python. In the short term, consider using this library only in environments where timing measurements by attackers are infeasible. In the medium term, implement your own wrappers around critical operations using constant-time libraries in languages like Rust, Go, or C. In the long term, wait for the planned Rust implementation mentioned in the library documentation that will properly address these issues.
Source: NVD (NIST), OSV (Open Source Vulnerabilities), cvelistv5, CVE.org (MITRE)
Affected products
The following versions are affected. Older releases of the same product line are also vulnerable unless stated otherwise.
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| DavidOsipov | PostQuantum-Feldman-VSS |
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| pypi | postquantum-feldman-vss |
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Weakness classes (CWE)
CWE-203Observable Discrepancy
Base
The product behaves differently or sends different responses under different circumstances in a way that is observable to an unauthorized actor.
Entry at MITRE CWE-203CWE-208Observable Timing Discrepancy
Base
Two separate operations in a product require different amounts of time to complete, in a way that is observable to an actor and reveals security-relevant information about the state of the product, such as whether a particular operation was successful or not.
Entry at MITRE CWE-208CWE-385Covert Timing Channel
Base
Covert timing channels convey information by modulating some aspect of system behavior over time, so that the program receiving the information can observe system behavior and infer protected information.
Entry at MITRE CWE-385
References & sources
- github.comDavidOsipov/PostQuantum-Feldman-VSS/security/advisories/GHSA-q6…web, x_refsource_CONFIRM
- NVD (NIST)vuln/detail/CVE-2025-29780advisory
- en.wikipedia.orgwiki/Side-channel_attackweb, x_refsource_MISC
- github.comDavidOsipov/PostQuantum-Feldman-VSSpackage
- rambus.comwp-content/uploads/2015/08/TimingAttacks.pdfweb, x_refsource_MISC
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<a href="https://intel.neosec.eu/en/cve/CVE-2025-29780"><img src="https://intel.neosec.eu/badge/cve/CVE-2025-29780.svg?lang=en" alt="CVE-2025-29780 – NEOSEC Intel" height="20"></a>