CVE-2025-4767 is a code injection vulnerability identified in the defog-ai introspect product, specifically affecting versions 0.1.0 through 0.1.4. The vulnerability resides in the function test_custom_tool within the file introspect/backend/integration_routes.py, part of the Test Endpoint component. The root cause is the improper handling of the input_model argument, which allows an attacker to inject and execute arbitrary code. This vulnerability requires local access with low privileges (PR:L) and does not require user interaction (UI:N). The CVSS 4.0 base score is 4.8, indicating a medium severity level. The attack vector is local (AV:L), meaning the attacker must have some form of local access to the system to exploit the flaw. The vulnerability impacts confidentiality, integrity, and availability at a low level (VC:L, VI:L, VA:L), suggesting that successful exploitation could lead to limited unauthorized access or disruption. No known exploits are currently reported in the wild, and no patches or fixes have been publicly linked yet. The vulnerability was publicly disclosed on May 16, 2025, and the exploit details are available, which could increase the risk of exploitation over time. The lack of authentication requirements and user interaction simplifies exploitation for a local attacker, but the necessity of local access limits the threat scope primarily to insiders or attackers who have already compromised the system to some extent.

For European organizations, the impact of CVE-2025-4767 depends largely on the deployment of defog-ai introspect within their environments. Organizations using this tool for AI model testing or integration could face risks of unauthorized code execution, potentially leading to data leakage, manipulation of AI model outputs, or disruption of AI-related services. Given the local access requirement, the threat is more significant in environments where multiple users have local system access or where attackers can gain initial footholds through other means. Confidentiality, integrity, and availability impacts are limited but non-negligible, especially in sensitive sectors such as finance, healthcare, or critical infrastructure where AI tools might be integrated into operational workflows. The medium severity rating suggests that while the vulnerability is not immediately critical, it could be leveraged as part of a multi-stage attack chain. European organizations with strict data protection regulations (e.g., GDPR) must consider the potential compliance risks if exploitation leads to unauthorized data access or manipulation.

1. Restrict local access to systems running defog-ai introspect to trusted personnel only, employing strict access controls and monitoring. 2. Implement application-level input validation and sanitization for the input_model argument to prevent code injection. 3. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to detect anomalous code execution attempts locally. 4. Monitor logs and system behavior for unusual activity related to the introspect backend, especially around the test_custom_tool function. 5. Isolate AI testing environments from critical production systems to limit potential lateral movement. 6. Stay updated with vendor advisories for patches or updates addressing this vulnerability and apply them promptly once available. 7. Conduct regular security audits and penetration tests focusing on local privilege escalation and code injection vectors within AI tooling environments. 8. Educate internal users about the risks of local exploitation and enforce the principle of least privilege for system access.