CVE-2026-5631
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-5631 is a medium-severity Injection (CWE-74) vulnerability. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 24th 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 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-2026-5631 is a code injection vulnerability in the open-source project assafelovic/gpt-researcher, affecting versions up to 3.4.3. The issue resides in the extract_command_data function within the file backend/server/server_utils.py, specifically in the ws Endpoint component. By manipulating the args argument, an attacker can inject arbitrary code remotely. The vulnerability is rated with a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L) and is associated with CWE-74 (Improper Neutralization of Special Elements used in an SQL Command) and CWE-94 (Improper Control of Generation of Code).
Remote attackers require no privileges or user interaction to exploit this vulnerability over the network with low complexity. Successful exploitation allows limited impacts on confidentiality, integrity, and availability, enabling code injection that could lead to further compromise depending on the execution context.
References indicate the vulnerability was reported to the project via GitHub issue #1694, but the maintainers have not yet responded or released a patch. The exploit has been publicly disclosed and may be actively used, as noted in VulDB entries. No specific mitigations or workarounds are detailed in the available advisories.
Notably, gpt-researcher is an AI-powered research tool leveraging large language models, making this vulnerability relevant to AI/ML deployments where WebSocket endpoints may expose backend services. There is no confirmed evidence of widespread real-world exploitation at the time of publication on 2026-04-06.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19186
Vulnerability Data
A vulnerability has been found in assafelovic gpt-researcher up to 3.4.3. This affects the function extract_command_data of the file backend/server/server_utils.py of the component ws Endpoint. Such manipulation of the argument args leads to code injection. The attack may be performed…
more
from remote. The exploit has been disclosed to the public and may be used. The project was informed of the problem early through an issue report but has not responded yet.
- 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: gpt
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.
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.
Secure SDLC practices directly require input validation and output encoding that prevent injection flaws.
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.
Security testing in development catches injection vulnerabilities before release.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection attacks in software design.
Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.