Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-33752 is a high-severity SSRF (CWE-918) vulnerability in Lexiforest Curl Cffi. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 39th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2026-33752 is a server-side request forgery vulnerability (CWE-918) in curl_cffi, a Python binding for libcurl, affecting versions prior to 0.15.0. The issue arises because curl_cffi does not restrict HTTP requests to internal IP ranges and automatically follows redirects via the underlying libcurl implementation. This enables redirection from attacker-controlled URLs to sensitive internal services, such as cloud metadata endpoints. Furthermore, curl_cffi's TLS impersonation feature can make these requests mimic legitimate browser traffic, potentially bypassing network security controls.
The vulnerability carries a CVSS v3.1 base score of 8.6 (AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N), indicating exploitation by unauthenticated remote attackers with low complexity and no user interaction. An attacker who controls a URL fetched by an application using vulnerable curl_cffi can trigger redirects to internal resources, achieving high-impact confidentiality breaches through unauthorized data access, such as instance metadata in cloud environments.
The vulnerability is fixed in curl_cffi version 0.15.0. Advisory details are available in the GitHub security advisory at https://github.com/lexiforest/curl_cffi/security/advisories/GHSA-qw2m-4pqf-rmpp, which practitioners should consult for patching guidance and additional context.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19293
Vulnerability Data
curl_cffi is the a Python binding for curl. Prior to 0.15.0, curl_cffi does not restrict requests to internal IP ranges, and follows redirects automatically via the underlying libcurl. Because of this, an attacker-controlled URL can redirect requests to internal services…
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such as cloud metadata endpoints. In addition, curl_cffi’s TLS impersonation feature can make these requests appear as legitimate browser traffic, which may bypass certain network controls. This vulnerability is fixed in 0.15.0.
- CWE(s)
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.
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.
Secure development practices directly include input validation and destination allow-listing that prevent SSRF.
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.