Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-8156 is a critical-severity Command Injection (CWE-77) vulnerability in Agpt Autogpt Classic. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 25% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Supply Chain and Deployment 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-2024-8156 is a command injection vulnerability (CWE-77) in the workflow-checker.yml workflow of the significant-gravitas/autogpt GitHub repository. The issue arises from the insecure use of untrusted user input from `github.head.ref`, which allows arbitrary command injection. This affects versions up to and including the latest version, with a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H).
An unauthenticated remote attacker can exploit the vulnerability by creating a GitHub branch name containing a malicious payload and opening a pull request to the repository. Successful exploitation enables arbitrary command execution, potentially leading to reverse shell access or theft of sensitive tokens and keys.
A patch addressing the vulnerability is available in commit 1df7d527dd37dff8363dc162fb58d300f072e302 at https://github.com/significant-gravitas/autogpt/commit/1df7d527dd37dff8363dc162fb58d300f072e302. Further details are provided on the Huntr bounty page at https://huntr.com/bounties/959efe87-f109-4cef-94d8-90ff2c7aef51.
AutoGPT is an AI agent framework, highlighting the vulnerability's relevance to AI/ML repositories relying on GitHub Actions workflows.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6892
Vulnerability Data
A command injection vulnerability exists in the workflow-checker.yml workflow of significant-gravitas/autogpt. The untrusted user input `github.head.ref` is used insecurely, allowing an attacker to inject arbitrary commands. This vulnerability affects versions up to and including the latest version. An attacker can…
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exploit this by creating a branch name with a malicious payload and opening a pull request, potentially leading to reverse shell access or theft of sensitive tokens and keys.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: autogpt
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.3V1.2.5V1.2.8V1.2.9
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover command-construction flaws before deployment.
Input validation directly stops construction of commands from untrusted data containing special elements.
Secure engineering principles include proper neutralization and safe command construction practices.
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 validation and neutralization that prevent command injection.
Runtime monitoring of software and data can detect anomalous command execution resulting from injection.
Identifying recorded vulnerabilities enables remediation of command-injection flaws before exploitation.
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.
Secure coding standards require proper escaping and parameterization of commands, directly eliminating CWE-77.
Security testing in development catches command-injection vulnerabilities before release.
Secure development life cycle mandates input validation and command construction practices that directly prevent command injection.
Application security requirements explicitly call for controls against injection flaws including command injection.
Secure architecture principles reduce the attack surface but do not prescribe the specific neutralization techniques needed.
Environment separation limits the blast radius of an exploited command injection but does not prevent the flaw itself.