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| Property | Value |
|---|---|
| Advisory ID | SGZ-2026-98652 |
| Severity | MEDIUM |
| CWE | CWE-327 |
| Confidence | 80% |
| Category | crypto_misuse |
| Exploitability | possible |
| Package | netmd-js@latest (Node.js) |
| Location | tools/gen_crypto_vectors.ts:10-15 |
| Affected Functions | mulberry32, randBytes, hex, wordArrayToByteArray |
| Attack Vector | LOCAL |
| Attack Complexity | LOW |
| Privileges Required | NONE |
| Discovered By | SafeGuard Zero-Day AI Discovery Engine |
The mulberry32 function uses a predictable random number generator, which can lead to predictable encryption keys.
The vulnerability was identified in the file tools/gen_crypto_vectors.ts at lines 10-15 within the netmd-js package (version latest). The following functions are directly affected: mulberry32, randBytes, hex, wordArrayToByteArray. Any code path that invokes these functions inherits this vulnerability.
File: tools/gen_crypto_vectors.ts (lines 10-15)
const rand = mulberry32(0xc0ffee);
const randBytes = (n: number) =>
new Uint8Array(new Array(n).fill(0).map(() => Math.floor(rand() * 256)));
The code above demonstrates the vulnerable pattern. This code is executed at runtime and can be directly exploited by an attacker with the appropriate access level.
An attacker can exploit this vulnerability by predicting the encryption keys used by the mulberry32 function.
Predictable encryption keys can lead to compromised data security.
Use a cryptographically secure random number generator.
Advisory: SGZ-2026-98652 | Source: SafeGuard Zero-Day AI Discovery | Status: Candidate
This vulnerability was autonomously discovered by SafeGuard's AI-powered Zero-Day Discovery engine using TAOR (Think-Act-Observe-Repeat) agentic analysis on the package source code. It is not yet tracked in any public vulnerability database (CVE, NVD, GHSA, OSV). This finding should be triaged by a security engineer and, if confirmed, reported upstream to the package maintainer.
| Property | Value |
|---|---|
| Advisory ID | SGZ-2026-98652 |
| Severity | MEDIUM |
| CWE | CWE-327 |
| Confidence | 80% |
| Category | crypto_misuse |
| Exploitability | possible |
| Package | netmd-js@latest (Node.js) |
| Location | tools/gen_crypto_vectors.ts:10-15 |
| Affected Functions | mulberry32, randBytes, hex, wordArrayToByteArray |
| Attack Vector | LOCAL |
| Attack Complexity | LOW |
| Privileges Required | NONE |
| Discovered By | SafeGuard Zero-Day AI Discovery Engine |
The mulberry32 function uses a predictable random number generator, which can lead to predictable encryption keys.
The vulnerability was identified in the file tools/gen_crypto_vectors.ts at lines 10-15 within the netmd-js package (version latest). The following functions are directly affected: mulberry32, randBytes, hex, wordArrayToByteArray. Any code path that invokes these functions inherits this vulnerability.
File: tools/gen_crypto_vectors.ts (lines 10-15)
const rand = mulberry32(0xc0ffee);
const randBytes = (n: number) =>
new Uint8Array(new Array(n).fill(0).map(() => Math.floor(rand() * 256)));
The code above demonstrates the vulnerable pattern. This code is executed at runtime and can be directly exploited by an attacker with the appropriate access level.
An attacker can exploit this vulnerability by predicting the encryption keys used by the mulberry32 function.
Predictable encryption keys can lead to compromised data security.
Use a cryptographically secure random number generator.
Advisory: SGZ-2026-98652 | Source: SafeGuard Zero-Day AI Discovery | Status: Candidate
This vulnerability was autonomously discovered by SafeGuard's AI-powered Zero-Day Discovery engine using TAOR (Think-Act-Observe-Repeat) agentic analysis on the package source code. It is not yet tracked in any public vulnerability database (CVE, NVD, GHSA, OSV). This finding should be triaged by a security engineer and, if confirmed, reported upstream to the package maintainer. This vulnerability involves weaknesses in
This medium-severity vulnerability could be exploited under certain conditions to compromise security controls or access sensitive information. Should be addressed in a timely manner.
Isolate affected systems from untrusted networks until patching is complete
Implement enhanced monitoring for exploitation attempts and unusual behavior