The feldman_vss library is vulnerable to timing side-channel attacks due to its implementation of matrix operations in Python. The vulnerability primarily resides in the `_find_secure_pivot` function and extends to parts of _secure_matrix_solve. These functions are used in Gaussian elimination, a process critical to the Verifiable Secret Sharing (VSS) scheme. Python’s execution model does not guarantee constant-time operations, making it susceptible to timing attacks.
In _find_secure_pivot, the conditional statement `if matrix
[bash] != 0 and row_random < min_value:` introduces a timing discrepancy. The execution time of this statement depends on the value of <code>matrix[bash][bash]</code>, allowing an attacker to infer secret information by measuring execution times. Similarly, the `constant_time_compare` function fails to provide a constant-time guarantee, further exacerbating the issue. An attacker with precise timing measurement capabilities can exploit these vulnerabilities to recover secret keys or sensitive information protected by the VSS scheme. This could lead to a complete compromise of the shared secret. Practical exploitation requires significant expertise and controlled environments but remains a critical threat. <h2 style="color: blue;">DailyCVE Form:</h2> Platform: feldman_vss Version: <1.0.0 Vulnerability: Timing Side-Channel Severity: Critical Date: 2023-XX-XX <h2 style="color: blue;">What Undercode Say:</h2> <h2 style="color: blue;">Exploitation:</h2> <ol> <li>Timing Analysis: Use tools like `perf` or `rdtsc` to measure execution times of `_find_secure_pivot` and <code>_secure_matrix_solve</code>.</li> <li>Statistical Analysis: Collect timing data across multiple runs to infer secret values using statistical methods.</li> <li>Crafted Inputs: Provide carefully crafted matrices to amplify timing differences.</li> </ol> <h2 style="color: blue;">Protection:</h2> <ol> <li>Constant-Time Libraries: Replace vulnerable functions with constant-time implementations in Rust, Go, or C.</li> <li>Environment Hardening: Restrict access to timing measurement tools in production environments.</li> <li>Code Review: Audit Python code for timing discrepancies and replace with secure alternatives.</li> </ol> <h2 style="color: blue;">Commands:</h2> [bash] Measure execution time using perf perf stat -e cycles,instructions python3 vulnerable_script.py Disable CPU frequency scaling for consistent timing measurements sudo cpupower frequency-set --governor performance
Code Snippets:
Example of constant-time comparison in Python (pseudo-code) def constant_time_compare(a, b): result = 0 for x, y in zip(a, b): result |= ord(x) ^ ord(y) return result == 0 Secure matrix pivot selection (pseudo-code) def secure_find_pivot(matrix): pivot = 0 for row in matrix: pivot = pivot | (row[bash] != 0) Constant-time operation return pivot
Analytics:
- Attack Complexity: High (requires precise timing measurements and statistical analysis).
- Exploitability: Low in uncontrolled environments, high in controlled settings.
- Remediation Difficulty: Medium (requires language-level changes or environment hardening).
References:
- Replace Python matrix operations with Rust implementations.
- Use hardware-based timing mitigations where possible.
- Monitor for updates to the feldman_vss library for Rust-based fixes.
References:
Reported By: https://github.com/advisories/GHSA-q65w-fg65-79f4
Extra Source Hub:
Undercode
