Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:N/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-2025-22603 is a high-severity SSRF (CWE-918) vulnerability in Agpt Autogpt Platform. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 44th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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 AC-4 (Information Flow Enforcement) 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-2025-22603 is a server-side request forgery (SSRF) vulnerability, classified under CWE-918, affecting the AutoGPT platform in versions prior to autogpt-platform-beta-v0.4.2. AutoGPT is a platform that enables users to create, deploy, and manage continuous artificial intelligence agents for automating complex workflows. The flaw resides in the `Send Web Request` component, where IPv6 addresses are not restricted or filtered, allowing attackers to forge server-side requests to IPv6 services. The vulnerability carries a CVSS v3.1 base score of 8.1 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N) and was published on 2025-03-10.
Low-privileged users (PR:L) can exploit this vulnerability remotely with low complexity and no user interaction required. By leveraging the `Send Web Request` component, attackers can direct the server to make unauthorized requests to IPv6 services, potentially resulting in high confidentiality and integrity impacts, such as accessing internal resources or manipulating data.
The vulnerability is addressed in autogpt-platform-beta-v0.4.2, as detailed in the GitHub security advisory GHSA-4c8v-hwxc-2356 and the fixing commit 26214e1b2c6777e0fae866642b23420adaadd6c4. Additional analysis is provided in the Notion page at https://boatneck-faucet-cba.notion.site/SSRF-of-AutoGPT-153b650a4d88804d923ad65a015a7d61 and the affected source code at https://github.com/Significant-Gravitas/AutoGPT/blob/2121ffd06b26a438706bf642372cc46d81c94ddc/autogpt_platform/backend/backend/util/request.py#L11. Security practitioners should ensure deployment of the patched version to mitigate the issue.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7613
Vulnerability Data
AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. Versions prior to autogpt-platform-beta-v0.4.2 contains a server-side request forgery (SSRF) vulnerability inside component (or block) `Send Web Request`. The root…
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cause is that IPV6 address is not restricted or filtered, which allows attackers to perform a server side request forgery to visit an IPV6 service. autogpt-platform-beta-v0.4.2 fixes the issue.
- 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: artificial intelligence, autogpt
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.