CVE-2026-3693
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-2026-3693 is a medium-severity Resource Injection (CWE-99) vulnerability. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 33th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and AC-3 (Access Enforcement) — 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-2026-3693 is a vulnerability in Shy2593666979 AgentChat versions up to 2.3.0, affecting the get_user_info and update_user_info functions in the file /src/backend/agentchat/api/v1/user.py within the User Endpoint component. The flaw stems from improper control of resource identifiers (CWE-99), triggered by manipulation of the user_id argument.
The vulnerability enables remote exploitation by unauthenticated attackers (PR:N) with low attack complexity (AC:L) and no user interaction (UI:N), yielding 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). Attackers can achieve low impacts on confidentiality, integrity, and availability, likely allowing unauthorized access, modification, or disruption of user resources by bypassing identifier controls.
Advisories note that the vendor was contacted early regarding this disclosure but provided no response, with no patches or mitigations mentioned. Exploits have been published and are available in GitHub repositories such as https://github.com/CC-T-454455/Vulnerabilities/tree/master/agent-chat/vulnerability-1 and https://github.com/CC-T-454455/Vulnerabilities/tree/master/agent-chat/vulnerability-2, alongside details on VulDB at https://vuldb.com/?ctiid.349640, https://vuldb.com/?id.349640, and https://vuldb.com/?submit.765589.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-10201
Vulnerability Data
A flaw has been found in Shy2593666979 AgentChat up to 2.3.0. This issue affects the function get_user_info/update_user_info of the file /src/backend/agentchat/api/v1/user.py of the component User Endpoint. This manipulation of the argument user_id causes improper control of resource identifiers. It is…
more
possible to initiate the attack remotely. The exploit has been published and may be used. The vendor was contacted early about this disclosure but did not respond in any way.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V1.3.8V1.3.9V1.3.11V9.2.2
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation of inputs before they are accepted as resource identifiers.
Enforces authorizations on resource access so an injected identifier cannot reach outside the intended sphere.
Enforces information flow rules that block use of untrusted identifiers to reach unauthorized resources.
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 sanitization that prevent resource-identifier injection flaws.
Least-privilege access policy and enforcement limits damage from injected resource identifiers even when input validation is absent.
Network segmentation and unauthorized-access controls reduce the blast radius of successful resource injection.
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 can detect resource-injection flaws but does not itself implement preventive controls.
Secure SDLC mandates input validation and resource-identifier sanitization that directly prevents resource injection.
Application security requirements explicitly call for controls on external identifiers used to access resources.
Secure architecture principles reduce attack surface for resource injection but do not prescribe identifier validation.
Secure coding standards require strict validation and whitelisting of all resource identifiers before use.
Information-access-restriction policies limit which resources can be referenced, indirectly reducing injection impact.