CVE-2025-29780
Raw vector
CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:N/VC:H/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-29780 is a medium-severity Observable Discrepancy (CWE-203) vulnerability. Its CVSS base score is 5.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Password Guessing (T1110.001); ranked at the 14th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to IA-6 (Authentication Feedback) and SA-11 (Developer Testing and Evaluation) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6671
Vulnerability Data
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…
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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.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 1 hardening rule · 1 OS baseline
V11.2.4
Mitigating Controls (NIST 800-53 r5) AI
Obscures authentication feedback so that success/failure differences are not observable to attackers.
Developer testing can include timing analysis or side-channel test cases that reveal observable timing discrepancies.
Covert channel analysis directly identifies timing channels that could leak information.
Requires error messages to avoid revealing exploitable details, directly stopping observable response discrepancies.
Engineering principles can mandate constant-time algorithms and side-channel resistance so timing discrepancies are never introduced.
Requiring approved cryptographic modules and algorithms implicitly demands implementations free of observable timing leaks.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent observable response discrepancies via consistent error handling and timing.
Runtime monitoring of hardware/software behavior can detect anomalous timing patterns that indicate covert channels.
Vulnerability identification processes can surface timing-channel weaknesses during design or code analysis.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Detailed logging can reveal timing anomalies but does not prevent covert timing channels.
Continuous monitoring may detect timing-based exfiltration but does not eliminate the channel itself.
Network segmentation reduces attack surface but does not address intra-process timing channels.
Network segregation limits external timing observation but not internal covert timing.
Secure architecture principles can include timing-channel countermeasures but are not specific.
Secure coding guidelines may recommend constant-time algorithms but coverage is not guaranteed.