Vulnerabilities & CVEs SecurityWeek 1 week ago

Exploitation Hits Rejetto HFS Vulnerability Discovered by AI

CN
CyberNew Threat Research Desk
• Original Reporting: SecurityWeek
CyberNew Threat Intel Assessment
Assessed Severity: CRITICAL
Impacted Surface
Enterprise Perimeter & Network Defense
Intelligence Sector
Vulnerabilities & CVEs

Analyst Takeaway

"Threat actors are exploiting a critical vulnerability in Rejetto HTTP File Server (HFS) to bypass authentication and gain remote code execution (RCE), VulnCheck warns. Tracked as CVE-2026-61500 (CVSS score of 9.3), the flaw exists because the open source file server discloses outputs of its non-cryptographic session cookie generator to unauthenticated clients during login. It also derives the..."

Recommended Defense & Mitigation

  • Emergency Patching: Prioritize vendor patches and CVE mitigation across internet-facing assets immediately.
  • Perimeter Audit: Inspect perimeter firewall logs and hunt for unauthorized RCE indicators.
Curated & Verified by CyberNew Threat Intelligence Desk Standard Defense Framework (NIST / MITRE ATT&CK)
Vulnerability

Threat actors are exploiting a critical vulnerability in Rejetto HTTP File Server (HFS) to bypass authentication and gain remote code execution (RCE), VulnCheck warns.

Tracked as CVE-2026-61500 (CVSS score of 9.3), the flaw exists because the open source file server discloses outputs of its non-cryptographic session cookie generator to unauthenticated clients during login. It also derives the session-cookie signing key from the same generator.

The sensitive information leak allows an attacker to reconstruct the generator’s state and recover the signing key using a small set of collected login responses.

Using the recovered key, the attacker can then forge valid administrator session cookies to gain elevated access to the server and RCE via the server_code configuration feature.

The issue was that Rejetto HFS’s generator, Math.random(), was using the xorshift128+ algorithm to generate the ‘random’ value that was then passed to the server’s Node.js web framework Koa for signing session cookies.  

Because the algorithm’s outputs are reversible, an attacker able to collect other numbers generated by Math.random() could determine other generated numbers and forge the authentication cookies, cybersecurity firm Horizon3 explained in a technical report.

Advertisement. Scroll to continue reading.

Horizon3.ai researchers uncovered the flaw using Anthropic’s Mythos AI model, which used advanced mathematical reasoning to recognize that Math.random() PRNG outputs could be reversed to reconstruct the secret session-cookie signing key.

Written By Ionut Arghire

Source Verification & Original Intelligence

This threat report was reviewed by CyberNew analysts and cross-referenced with primary security disclosures from SecurityWeek.

Primary Advisory →