CVE-2024-6038
Gaizhenbiao Chuanhuchatgpt 20240410
Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2024-6038 is a high-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability in Gaizhenbiao Chuanhuchatgpt. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 48th 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 LLM Application Platforms; in the Other ATLAS/OWASP Terms 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.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-47196
Vulnerability Data
A Regular Expression Denial of Service (ReDoS) vulnerability exists in the latest version of gaizhenbiao/chuanhuchatgpt. The vulnerability is located in the filter_history function within the utils.py module. This function takes a user-provided keyword and attempts to match it against chat…
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history filenames using a regular expression search. Due to the lack of sanitization or validation of the keyword parameter, an attacker can inject a specially crafted regular expression, leading to a denial of service condition. This can cause severe degradation of service performance and potential system unavailability.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- chuanhuchatgpt is a self-hosted open-source UI for ChatGPT-like LLM interactions, classified as an enterprise AI assistant platform. The ReDoS vulnerability in its chat history filtering function affects this AI application, reported on an AI/ML bug bounty platform (huntr).
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.