diff --git a/advisories/github-reviewed/2025/03/GHSA-r8gc-qc2c-c7vh/GHSA-r8gc-qc2c-c7vh.json b/advisories/github-reviewed/2025/03/GHSA-r8gc-qc2c-c7vh/GHSA-r8gc-qc2c-c7vh.json
index b8afd0db40a..d217f98449f 100644
--- a/advisories/github-reviewed/2025/03/GHSA-r8gc-qc2c-c7vh/GHSA-r8gc-qc2c-c7vh.json
+++ b/advisories/github-reviewed/2025/03/GHSA-r8gc-qc2c-c7vh/GHSA-r8gc-qc2c-c7vh.json
@@ -1,7 +1,7 @@
 {
   "schema_version": "1.4.0",
   "id": "GHSA-r8gc-qc2c-c7vh",
-  "modified": "2025-03-14T19:55:18Z",
+  "modified": "2025-03-19T15:25:31Z",
   "published": "2025-03-14T19:55:17Z",
   "aliases": [
     "CVE-2025-29779"
diff --git a/advisories/github-reviewed/2025/03/GHSA-v432-7f47-9g94/GHSA-v432-7f47-9g94.json b/advisories/github-reviewed/2025/03/GHSA-v432-7f47-9g94/GHSA-v432-7f47-9g94.json
index 39f1cb049a9..57907650076 100644
--- a/advisories/github-reviewed/2025/03/GHSA-v432-7f47-9g94/GHSA-v432-7f47-9g94.json
+++ b/advisories/github-reviewed/2025/03/GHSA-v432-7f47-9g94/GHSA-v432-7f47-9g94.json
@@ -1,11 +1,11 @@
 {
   "schema_version": "1.4.0",
   "id": "GHSA-v432-7f47-9g94",
-  "modified": "2025-03-17T14:48:07Z",
+  "modified": "2025-03-19T15:24:59Z",
   "published": "2025-03-17T14:48:07Z",
   "aliases": [],
   "summary": "PostQuantum-Feldman-VSS'S Dependency Vulnerability in gmpy2 Leading to Interpreter Crash",
-  "details": "## Summary\nPostQuantum-Feldman-VSS, in versions ≤0.7.6b0, is vulnerable due to its dependency on gmpy2, which can crash the Python interpreter if memory allocation fails. This can be exploited for denial-of-service attacks, impacting the availability of systems using the library.\n\n## Description\nThe vulnerability arises from gmpy2’s behavior, as noted in its documentation: \n\n> gmpy2 can crash the Python interpreter in case of memory allocation failure ([gmpy2 Overview](https://gmpy2.readthedocs.io/en/latest/overview.html))\n\nThis stems from gmpy2’s reliance on the GMP library, which terminates the program when memory allocation fails ([GMP Memory Management](https://gmplib.org/manual/Memory-Management)). An attacker can exploit this by crafting inputs that exhaust memory, causing the interpreter to crash and disrupting service for legitimate users.\n\n## Impact\n- **Availability**: High impact, as the interpreter crash leads to complete denial of service.\n- **Confidentiality and Integrity**: No direct impact, as the vulnerability does not allow unauthorized access or data modification.\n\n## Affected Versions\n- All versions of PostQuantum-Feldman-VSS ≤0.7.6b0\n\n## Patched Versions\n- Versions >0.7.6b0 (e.g., 0.7.7 or later, once released with the `SafeGMPy2` implementation)\n\n## Workarounds (For Vulnerable Versions)\nFor users unable to upgrade to a patched version:\n- **Estimate Memory Usage**: Manually estimate memory requirements before operations and ensure sufficient resources.\n- **Monitor System Resources**: Implement monitoring to detect and respond to memory exhaustion attempts.\n- **Custom Memory Allocation**: Use GMP’s custom allocation functions to handle failures gracefully ([GMP Custom Allocation](https://gmplib.org/manual/Custom-Allocation)).\n\n## Mitigation Details\nThe fix introduces a `SafeGMPy2` wrapper that:\n1. Checks input sizes against configurable limits (e.g., `DEFAULT_MAX_BITS_POWMOD = 20000`).\n2. Estimates output sizes to prevent excessive memory use (e.g., in multiplication and powmod).\n3. Wraps critical gmpy2 functions (mpz, powmod, invert) with try-except blocks to raise `MemorySafetyError` instead of crashing.\n4. Monkey-patches gmpy2 to enforce these safety checks globally.\nThis has been integrated into key classes like `CyclicGroup` and `FeldmanVSS`, ensuring memory-safe cryptographic operations.\n\n## References\n- [gmpy2 Overview Documentation](https://gmpy2.readthedocs.io/en/latest/overview.html)\n- [GMP Memory Management Documentation](https://gmplib.org/manual/Memory-Management)\n- [GMP Custom Allocation Documentation](https://gmplib.org/manual/Custom-Allocation)",
+  "details": "**Description:**\n\nPostQuantum-Feldman-VSS, a Python library implementing Feldman's Verifiable Secret Sharing scheme with post-quantum security, was vulnerable to denial-of-service attacks in versions up to and including 0.7.6b0.  This vulnerability stems from the library's reliance on the `gmpy2` library for arbitrary-precision arithmetic.  `gmpy2`, in turn, depends on the GNU Multiple Precision Arithmetic Library (GMP). GMP, by design, terminates the process when it cannot allocate memory.  An attacker could exploit this by providing carefully crafted inputs that cause `gmpy2` to attempt to allocate extremely large amounts of memory, leading to a crash of the Python interpreter and thus a denial of service.\n\n**Vulnerability Details:**\n\nThe core issue lies in the behavior of GMP (and thus, `gmpy2`) when memory allocation fails.  Instead of raising a standard Python exception that could be caught and handled, GMP terminates the entire process. This behavior is documented in the GMP and gmpy2 documentation:\n\n*   **GMP Memory Management:** [https://gmplib.org/manual/Memory-Management](https://gmplib.org/manual/Memory-Management) (States that the default behavior is to terminate the program.)\n*   **gmpy2 Overview:** [https://gmpy2.readthedocs.io/en/latest/overview.html](https://gmpy2.readthedocs.io/en/latest/overview.html) (Warns that `gmpy2` can crash the interpreter on memory allocation failure.)\n\nSpecific operations within the PostQuantum-Feldman-VSS library that were particularly vulnerable include:\n\n*   **Large Exponentiation (`exp`, `secure_exp`):**  Exponentiation with very large bases or exponents can lead to extremely large results, consuming excessive memory.\n*   **Multi-exponentiation (`efficient_multi_exp`):**  Similar to exponentiation, but with multiple bases and exponents, increasing the risk.\n*   **Matrix Operations (`_secure_matrix_solve`):**  Large matrices used in polynomial reconstruction could lead to significant memory usage.\n*   **Polynomial Evaluation (`_evaluate_polynomial`):** Evaluating polynomials with large coefficients or at large values of `x` could trigger excessive memory allocation.\n\n**Mitigations in 0.8.0b2:**\n\nVersion 0.8.0b2 implements *significant mitigations* to greatly reduce the risk of this denial-of-service vulnerability.  These mitigations **do not** completely eliminate the underlying issue (as that would require changes to GMP itself), but they make successful exploitation *far* more difficult.  The mitigations include:\n\n1.  **Memory Monitoring (`MemoryMonitor` class):**\n    *   A new `MemoryMonitor` class is introduced to track estimated memory usage throughout the library's operations.\n    *   This class allows setting a maximum memory limit (defaulting to 1024MB, but configurable).\n    *   It provides methods to `check_allocation`, `allocate`, and `release` memory, raising a `MemoryError` if an operation would exceed the configured limit.\n\n2.  **Memory Safety Checks (`check_memory_safety` function):**\n    *   A new `check_memory_safety` function is used to estimate the memory requirements of various `gmpy2` operations *before* they are executed.\n    *   This function considers the operation type (`exp`, `mul`, `pow`, `mod`, `polynomial`, `matrix`, `polynomial_eval`) and the bit lengths of the operands.\n    *   It uses conservative estimates and scaling factors to account for `gmpy2`'s internal overhead.\n    *   If the estimated memory usage exceeds the limit set by the `MemoryMonitor`, the operation is rejected *before* calling `gmpy2`, preventing the crash.\n\n3.  **Integration into Core Classes:**\n    *   The `CyclicGroup` and `FeldmanVSS` classes now use the `check_memory_safety` function before performing potentially memory-intensive operations like `exp`, `mul`, `efficient_multi_exp`, `_evaluate_polynomial`, and `_secure_matrix_solve`.\n\n4.  **Enhanced Input Validation and Error Handling:**\n    *  Added improved input validation in functions.\n    *  Raises custom exceptions like `SecurityError`, `SerializationError`, `VerificationError`, and `ParameterError` that include detailed information for forensics and debugging.\n\n5.  **Safer Defaults and Configuration:**\n    *   The library is configured to use safe primes and large bit lengths by default, reducing the likelihood of accidental misconfiguration leading to excessively large numbers.\n    *   The `VSSConfig` class allows users to customize the `prime_bits` and `cache_size`, enabling them to tailor the library to their specific memory constraints.\n\n**Limitations of Mitigations:**\n\n*   **Estimation Inaccuracy:** The `check_memory_safety` function relies on *estimations* of memory usage. While these estimations are conservative, they are not perfect.  It is still theoretically possible (though much less likely) for an operation to consume more memory than estimated, leading to a crash.\n*   **GMP Behavior:** The fundamental issue of GMP terminating the process on memory allocation failure remains.  The mitigations prevent most common cases, but a sufficiently determined attacker with knowledge of the estimation algorithm *might* still be able to craft an input that triggers a crash.\n*   **Not a Complete Fix:** Version 0.8.0b2 is a *mitigation*, not a complete *elimination* of the vulnerability.\n\n**Workarounds (for versions <= 0.7.6b0):**\n\nIf upgrading to version 0.8.0b2 (or later) is not immediately possible, the following workarounds can help reduce the risk:\n\n*   **Limit Input Sizes:**  Carefully restrict the size of inputs to the library, particularly the bit lengths of secrets, shares, and coefficients, and the threshold value (t).  Avoid using excessively large values.  Use the `check_memory_safety` function (available in 0.8.0b2) to manually check the memory usage.\n*   **Resource Monitoring:** Implement external monitoring of your application's memory usage.  If memory usage approaches dangerous levels, take action to prevent a crash (e.g., terminate the process, reject new requests, etc.).\n*   **Input Validation:** Thoroughly validate all inputs to the library, ensuring they are within expected ranges and of the correct types.\n*   **Rate Limiting:** Implement rate limiting to prevent an attacker from flooding your system with requests designed to consume excessive memory.\n*  **Custom Memory Allocation**:  *Advanced users* could potentially modify the GMP library to use custom allocation functions that raise Python exceptions instead of terminating the process.  This is a complex and potentially risky approach, and is **not recommended** for most users.  (See [GMP Custom Allocation Documentation](https://gmplib.org/manual/Custom-Allocation)).\n\n**Recommendations:**\n\n*   **Upgrade to 0.8.0b2 (or later) as soon as possible.** This is the *most important* step you can take.\n*   **Configure Memory Limits:**  Use the `MemoryMonitor` in 0.8.0b2 (or later) to set appropriate memory limits for your application.  Consider your system's available memory and the expected workload.\n*   **Monitor Memory Usage:**  Even with the mitigations, continue to monitor your application's memory usage and be prepared to handle potential memory exhaustion events.\n*   **Follow Security Best Practices:**  Implement robust input validation, rate limiting, and other security measures to protect your application from various attacks, not just this specific vulnerability.\n\n**Future Work:**\n\n*   **Rust Integration:**  The long-term solution is to implement the most memory-intensive and security-critical operations in a lower-level language like Rust, which provides more control over memory management and can avoid the problematic GMP behavior. This is planned for future versions.\n*   **More Precise Memory Estimation:**  Research and improve the accuracy of the memory estimation algorithms used in `check_memory_safety`.\n*   **Fuzz Testing:**  Conduct extensive fuzz testing to identify any remaining edge cases that could trigger excessive memory allocation.",
   "severity": [
     {
       "type": "CVSS_V4",