Raw vector
CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-6983 is a high-severity Code Injection (CWE-94) vulnerability in Mudler Localai. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 32% 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 LLM Application Platforms; 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.
mudler/localai version 2.17.1 contains a remote code execution vulnerability in its backend component. The flaw stems from the backend accepting inputs from sources other than the configuration file, which enables an attacker to supply a malicious binary. This is tracked as CWE-94 and carries a CVSS 3.0 score of 8.8.
An attacker with limited privileges can send crafted inputs over the network to upload and execute arbitrary code, resulting in full control of the host system. No user interaction is required once the attacker has network access to the LocalAI instance.
The referenced commit d02a0f6f01d5c4a926a2d67190cb55d7aca23b66 addresses the issue, and details are available in the associated huntr.com bounty report. The EPSS score reached a peak of 0.0648 before receding to the current value of 0.0495.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-47964
Vulnerability Data
mudler/localai version 2.17.1 is vulnerable to remote code execution. The vulnerability arises because the localai backend receives inputs not only from the configuration file but also from other inputs, allowing an attacker to upload a binary file and execute malicious…
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code. This can lead to the attacker gaining full control over the system.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- LocalAI is an open-source, self-hosted API compatible with OpenAI for local inference of AI models including LLMs, making it an APIs and Models platform. The RCE vulnerability in its backend input handling confirms AI-related impact.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.