CVE-2024-58340
Langchain ≤ 0.3.1
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2024-58340 is a high-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability in Langchain Langchain. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 35th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as NLP and Transformers; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-58340 is a regular expression denial-of-service (ReDoS) vulnerability in LangChain versions up to and including 0.3.1. The flaw affects the MRKLOutputParser.parse() method in the file libs/langchain/langchain/agents/mrkl/output_parser.py, where a backtracking-prone regular expression is used to extract tool actions from model output. This can result in excessive CPU consumption when processing malicious input.
The vulnerability can be exploited by an attacker who can supply or influence the text parsed by MRKLOutputParser.parse(), for instance through prompt injection in downstream applications that pass LLM output directly to the parser. Successful exploitation triggers significant parsing delays and a denial-of-service condition due to high resource usage. It carries a CVSS v3.1 score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) and is associated with CWE-1333.
Advisories and references, including the LangChain GitHub repository (https://github.com/langchain-ai/langchain), Huntr (https://huntr.com/bounties/e7ece02c-d4bb-4166-8e08-6baf4f8845bb), LangChain website (https://www.langchain.com/), and VulnCheck (https://www.vulncheck.com/advisories/langchain-mrkloutputparser-redos), provide further details on the issue.
This vulnerability is relevant to AI/ML applications built with LangChain, particularly those involving agent tooling and LLM output parsing.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-2399
Vulnerability Data
LangChain versions up to and including 0.3.1 contain a regular expression denial-of-service (ReDoS) vulnerability in the MRKLOutputParser.parse() method (libs/langchain/langchain/agents/mrkl/output_parser.py). The parser applies a backtracking-prone regular expression when extracting tool actions from model output. An attacker who can supply or influence…
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the parsed text (for example via prompt injection in downstream applications that pass LLM output directly into MRKLOutputParser.parse()) can trigger excessive CPU consumption by providing a crafted payload, causing significant parsing delays and a denial-of-service condition.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: langchain, llm, prompt injection
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover inefficient regex patterns via performance or static analysis.
Development standards and tools can require safe regex construction and forbid known exponential patterns.
Denial-of-service protections limit resource exhaustion caused by expensive regex evaluation.
Input validation can constrain data that would otherwise trigger worst-case regex complexity.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing can detect and reject regex patterns with exponential worst-case complexity.
Secure development lifecycle mandates review of algorithmic efficiency, directly addressing ReDoS-prone regex.
Application security requirements can specify input-validation rules that limit regex complexity.
Secure architecture principles encourage avoidance of computationally expensive constructs such as catastrophic backtracking.
Secure coding standards explicitly prohibit or limit the use of inefficient regular expressions.