CVE-2026-24903: AlgoNetLab OrcaStatLLM-Researcher · Cross-site Scripting
Affects AlgoNetLab OrcaStatLLM-ResearcherSource CVE Database V5
Not known to be exploited · CVSS 5.3 Medium.
Data as of 26 Oct 2026, 14:05 UTC
- EPSS
- 0.1% chance of exploitation in 30 days – higher than 9% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:N/SC:L/SI:L/SA:NExploitation evidence
Disclosed: evidence found.
CVE published 6 Feb 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| AlgoNetLabOrcaStatLLM-Researcher | Affected
| Fixed in– |
GitHubalgonetlab/OrcaStatLLM-Researcherpkg:github/algonetlab/OrcaStatLLM-ResearcherOther threats | Affected
| Fixed in– |
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
To mitigate this vulnerability, organizations should implement strict input validation and sanitization on all user-supplied data, especially research topic inputs that are logged and displayed. Employ context-aware output encoding to neutralize any potentially malicious scripts before rendering in the browser. Utilize Content Security Policy (CSP) headers to restrict the execution of unauthorized scripts and reduce the impact of XSS attacks. Regularly audit and monitor logs and session pages for suspicious entries that could indicate exploitation attempts. Since no official patches are currently available, consider isolating or restricting access to the vulnerable OrcaStatLLM-Researcher instances until a fix is released. Educate users about the risks of clicking on untrusted links or session pages. Finally, implement web application firewalls (WAF) with rules designed to detect and block XSS payloads targeting this product.
Description
OrcaStatLLM Researcher is an LLM Based Research Paper Generator. A Stored Cross-Site Scripting (XSS) vulnerability was discovered in the Log Message in the Session Page in OrcaStatLLM-Researcher that allows attackers to inject and execute arbitrary JavaScript code in victims' browsers through malicious research topic inputs.