Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:NSummary
CVE-2025-0454 is a high-severity SSRF (CWE-918) vulnerability in Agpt Autogpt Platform. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 42th 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-2025-0454 is a Server-Side Request Forgery (SSRF) vulnerability affecting the Requests utility in significant-gravitas/autogpt versions prior to v0.4.0. The issue stems from a hostname confusion between the `urlparse` function from the `urllib.parse` library and the `requests` library, allowing specially crafted URLs to bypass SSRF protections. Assigned CWE-918, it carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N), highlighting high integrity impact from network-accessible exploitation with low complexity and no privileges required.
A malicious user can exploit this vulnerability by submitting a crafted URL, such as `http://localhost:\@google.com/../`, to the affected Requests utility. This bypasses the SSRF check, enabling the attacker to forge requests from the server to unintended destinations, potentially leading to unauthorized access to internal services or resources. Exploitation requires only the ability to provide input URLs to the application, with no user interaction or privileges needed.
The vulnerability was addressed in autogpt v0.4.0 via commit ff065cd24c2289878c0abdb9adbf91c305f0d70a, as documented on GitHub. Additional details, including the bounty report, are available on Huntr at https://huntr.com/bounties/0664fdee-bdc2-4650-8075-74d7b8d3e308. Security practitioners should ensure upgrades to v0.4.0 or later to mitigate the issue.
This flaw occurs in AutoGPT, an open-source framework for autonomous AI agents, underscoring SSRF risks in AI/ML tooling that processes external URLs. No public evidence of real-world exploitation has been reported as of the CVE publication on 2025-03-20.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6821
Vulnerability Data
A Server-Side Request Forgery (SSRF) vulnerability was identified in the Requests utility of significant-gravitas/autogpt versions prior to v0.4.0. The vulnerability arises due to a hostname confusion between the `urlparse` function from the `urllib.parse` library and the `requests` library. A malicious…
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user can exploit this by submitting a specially crafted URL, such as `http://localhost:\@google.com/../`, to bypass the SSRF check and perform an SSRF attack.
- 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: 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.