CVE-2024-52524
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/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:ClearSummary
CVE-2024-52524 is a medium-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked in the top 47% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other AI Platforms; in the Data-Related Vulnerabilities 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-2024-3295
Vulnerability Data
Giskard is an evaluation and testing framework for AI systems. A Remote Code Execution (ReDoS) vulnerability was discovered in Giskard component by the GitHub Security Lab team. When processing datasets with specific text patterns with Giskard detectors, this vulnerability could…
more
trigger exponential regex evaluation times, potentially leading to denial of service. Giskard versions prior to 2.15.5 are affected.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Giskard is an open-source evaluation and testing framework specifically for AI/ML systems, fitting the 'Other Platforms' category as it supports AI model scanning and testing but does not match more specific categories like frameworks or libraries.
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.