CVE-2025-58353
Published: 04 September 2025
Summary
CVE-2025-58353 is a high-severity Improper Input Validation (CWE-20) vulnerability. Its CVSS base score is 8.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 20.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the LLM/Generative AI Risks risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-26866
Vulnerability details
Promptcraft Forge Studio is a toolkit for evaluating, optimizing, and maintaining LLM-powered applications. All versions of Promptcraft Forge Studio sanitize user input using regex blacklists such as r`eplace(/javascript:/gi, '')`. Because the package uses multi-character tokens and each replacement is applied…
more
only once, removing one occurrence can create a new dangerous token due to overlap. The “sanitized” value may still contain an executable payload when used in href/src (or injected into the DOM). There is currently no fix for this issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: llm
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The input sanitization flaw allows bypassing regex blacklists via overlapping tokens, enabling injection of executable JavaScript payloads (e.g., javascript:) into href/src attributes or DOM for exploitation of the web application (T1190) and content injection (T1659).
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.