CVE-2024-46946: langchain-experimental · n/a
Affects langchain-experimentalSource CVE Database V5
Not known to be exploited · CVSS 9.8 Critical.
Data as of 25 Oct 2026, 19:02 UTC
- EPSS
- 1.3% chance of exploitation in 30 days – higher than 72% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 19 Sep 2024
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 2026-10-25.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
PyPIlangchain-experimentalpkg:pypi/langchain-experimentalOther 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
1. Immediately audit all deployments of LangChain Experimental to identify usage of versions 0.1.17 through 0.3.0, especially where LLMSymbolicMathChain is enabled. 2. Disable or isolate the LLMSymbolicMathChain component until a secure patch or update is released. 3. Avoid processing untrusted or user-supplied input through sympy.sympify or any feature that uses eval internally. 4. Implement strict input validation and sanitization layers before passing data to symbolic math processing functions. 5. Monitor network and system logs for unusual activity indicative of exploitation attempts, such as unexpected command executions or anomalous process behavior. 6. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to detect and block suspicious code execution. 7. Stay updated with LangChain project announcements for patches or security advisories and apply updates promptly once available. 8. Consider containerizing or sandboxing LangChain components to limit the blast radius of potential exploitation. 9. Educate developers and security teams about the risks of using eval and similar functions in code processing user input.
Description
langchain_experimental (aka LangChain Experimental) 0.1.17 through 0.3.0 for LangChain allows attackers to execute arbitrary code through sympy.sympify (which uses eval) in LLMSymbolicMathChain. LLMSymbolicMathChain was introduced in fcccde406dd9e9b05fc9babcbeb9ff527b0ec0c6 (2023-10-05).