CVE-2023-49286
Squid-Cache Squid ≤ 6.4
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:N/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-49286 is a high-severity Incorrect Check of Function Return Value (CWE-253) vulnerability in Squid-Cache Squid. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data Obfuscation (T1001); ranked in the top 4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-53279
Vulnerability Data
Squid is a caching proxy for the Web supporting HTTP, HTTPS, FTP, and more. Due to an Incorrect Check of Function Return Value bug Squid is vulnerable to a Denial of Service attack against its Helper process management. This bug…
more
is fixed by Squid version 6.5. Users are advised to upgrade. There are no known workarounds for this vulnerability.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Requires detection and response to audit logging failures as an unusual or exceptional condition.
Implements detection of unusual or exceptional conditions followed by safe mode entry, reducing the window for exploitation of unchecked conditions.
Training ensures users perform required checks for unusual or exceptional conditions as part of contingency roles, limiting attacker leverage from skipped validations.
IR testing directly validates checks for unusual or exceptional conditions that could indicate security incidents.
Requires ongoing monitoring of organization-defined metrics and analysis, enabling checks for unusual or exceptional conditions.
Security testing routinely checks for unusual or exceptional inputs/conditions, identifying missing validation steps that flaw remediation then resolves.
Requires detection of unusual conditions followed by a controlled transition to the defined failure state.
MTTF determination forces explicit checks for conditions that precede predictable component failure.
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 software can detect assertion-triggered crashes as adverse events.
Vulnerability identification processes can discover and record reachable-assertion flaws before deployment.
Secure SDLC practices directly enforce proper error-handling and return-value checking during development.
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.
Hardening callouts derived
DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.
- No fine-grained STIG row for CWE-754, a class-level weakness. Rules are authored against its narrower children: CWE-354.