CVE-2025-66577
Yhirose Cpp-Httplib ≤ 0.27.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:NSummary
CVE-2025-66577 is a medium-severity Improper Output Neutralization for Logs (CWE-117) vulnerability in Yhirose Cpp-Httplib. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 19th 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-24 (Access Control Decisions) and SA-11 (Developer Testing and Evaluation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-201454
Vulnerability Data
cpp-httplib is a C++11 single-file header-only cross platform HTTP/HTTPS library. Prior to 0.27.0, a vulnerability allows attacker-controlled HTTP headers to influence server-visible metadata, logging, and authorization decisions. An attacker can supply X-Forwarded-For or X-Real-IP headers which get accepted unconditionally by…
more
get_client_ip() in docker/main.cc, causing access and error logs (nginx_access_logger / nginx_error_logger) to record spoofed client IPs (log poisoning / audit evasion). This vulnerability is fixed in 0.27.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 1 hardening rule · 1 OS baseline
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Mitigating Controls (NIST 800-53 r5) AI
Explicitly requires that every access decision be based on authoritative, protected decision data rather than caller-supplied inputs.
Developer testing and evaluation can discover missing or incorrect output neutralization when log messages are constructed from untrusted input.
Input validation can reject malformed or attacker-controlled values before they reach a security decision point.
Access enforcement requires decisions to be made from trusted policy data rather than modifiable client-supplied inputs.
Information-flow enforcement applies rules to validated, internal attributes instead of untrusted external values.
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.
Strong authentication mechanisms directly prevent security decisions from depending on modifiable, untrusted inputs.
Verification and protection of identity assertions stops reliance on attacker-controlled values for authorization decisions.
Secure SDLC practices and coding standards directly require output sanitization for logs.
Proper management of identities/credentials reduces the chance that security decisions will be driven by untrusted inputs.
Policy-driven, least-privilege authorization reduces opportunities to bypass controls via tampered inputs.
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.
Secure coding standards directly prohibit using untrusted inputs for security-critical decisions.
Security testing can detect log injection flaws but does not prevent them at the source.
Logging control directly requires proper log generation and handling, which mitigates improper output neutralization.
Monitoring activities rely on trustworthy logs but do not ensure log message integrity.
Secure SDLC includes coding standards that reduce log-related weaknesses but does not specifically address logging.
Application security requirements explicitly call for validation of inputs used in security decisions.