CVE-2026-42079
Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2026-42079 is a high-severity Eval Injection (CWE-95) vulnerability. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique JavaScript (T1059.007); ranked at the 4th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — 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-2026-42079 is an arbitrary code execution vulnerability in PPTAgent, an agentic framework for reflective PowerPoint generation. Prior to commit 418491a, the framework executes LLM-generated code using Python's eval() function with builtins in scope, enabling attackers to run arbitrary Python code. The issue is classified as CWE-95 (Improper Neutralization of Special Elements used in an eval() or Similar Function while Processing User-Controlled Input) and carries a CVSS v3.1 base score of 8.6 (AV:L/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H).
An attacker with local access can exploit this vulnerability with low complexity and no required privileges, but it requires user interaction, such as tricking a user into processing malicious input that influences LLM code generation. Successful exploitation grants arbitrary code execution on the host system, resulting in high impacts to confidentiality, integrity, and availability, along with a scope change that affects the broader system.
The vulnerability has been patched in commit 418491a of the PPTAgent repository. Additional details are available in the GitHub security advisory GHSA-89g2-xw5c-v95p.
This flaw underscores risks in AI/ML agentic workflows where LLM outputs are directly evaluated, as seen in PPTAgent's PowerPoint generation pipeline. No public information on real-world exploitation is available.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-27015
Vulnerability Data
PPTAgent is an agentic framework for reflective PowerPoint generation. Prior to commit 418491a, PPTAgent is vulnerable to arbitrary code execution via Python eval() of LLM-generated code with builtins in scope. This issue has been patched via commit 418491a.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1426 — LLM output reaches eval() sink with no validation.
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Matched keywords: llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and code analysis can discover eval-injection flaws but does not stop their introduction.
Input validation explicitly requires neutralizing untrusted data before it reaches dynamic evaluation constructs such as eval.
Secure-development standards and tools can mandate safe coding patterns that avoid unsafe dynamic evaluation.
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.
Secure SDLC practices directly require input neutralization and avoidance of unsafe dynamic evaluation.
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 in development can detect eval injection vulnerabilities before deployment.
Secure development life cycle mandates input validation and safe coding practices that directly prevent eval injection.
Application security requirements include rules against dynamic code execution of untrusted input.
Secure architecture principles discourage unsafe dynamic evaluation constructs.
Secure coding explicitly requires neutralization of input before dynamic evaluation, directly mitigating eval injection.
Separation of environments limits the blast radius if eval injection occurs in non-production.