Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-55241 is a high-severity Code Injection (CWE-94) vulnerability in Notion (inferred from references). 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 45% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
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.
CVE-2024-55241 is a code injection vulnerability (CWE-94) affecting the deep-diver LLM-As-Chatbot application prior to commit 99c2c03. The flaw resides in the modelsbyom.py component, enabling remote arbitrary code execution. It carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to its network accessibility, low complexity, and significant impacts on confidentiality, integrity, and availability.
A remote attacker with low privileges, such as an authenticated user, can exploit this vulnerability over the network without requiring user interaction. Successful exploitation allows the attacker to execute arbitrary code on the affected system, potentially leading to full compromise including data exfiltration, modification of chatbot behavior, or system takeover.
Advisories, including a detailed Notion page at https://diamond-bath-fd4.notion.site/Remote-Code-Execution-vulnerability-in-load_model-in-deep-diver-LLM-As-Chatbot-14d4a5b4bb28806795e8e5e8ef9ae27b, describe the remote code execution issue in the load_model function of deep-diver LLM-As-Chatbot. Practitioners should update to commit 99c2c03 or later to mitigate the vulnerability.
This vulnerability is particularly relevant to AI/ML deployments, as it targets an LLM-as-chatbot framework, highlighting risks in loading models within untrusted or insufficiently validated environments. No public evidence of real-world exploitation has been reported as of the CVE publication on 2025-02-06.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-52758
Vulnerability Data
An issue in deep-diver LLM-As-Chatbot before commit 99c2c03 allows a remote attacker to execute arbitrary code via the modelsbyom.py component.
- 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
- Matched keywords: llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
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.