Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:LSummary
CVE-2024-25617 is a medium-severity Collapse of Data into Unsafe Value (CWE-182) vulnerability in Squid-Cache Squid. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique OS Exhaustion Flood (T1499.001); ranked in the top 0.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-22939
Vulnerability Data
Squid is an open source caching proxy for the Web supporting HTTP, HTTPS, FTP, and more. Due to a Collapse of Data into Unsafe Value bug ,Squid may be vulnerable to a Denial of Service attack against HTTP header parsing.…
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This problem allows a remote client or a remote server to perform Denial of Service when sending oversized headers in HTTP messages. In versions of Squid prior to 6.5 this can be achieved if the request_header_max_size or reply_header_max_size settings are unchanged from the default. In Squid version 6.5 and later, the default setting of these parameters is safe. Squid will emit a critical warning in cache.log if the administrator is setting these parameters to unsafe values. Squid will not at this time prevent these settings from being changed to unsafe values. Users are advised to upgrade to version 6.5. There are no known workarounds for this vulnerability. This issue is also tracked as SQUID-2024:2
- 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
V15.3.4
Mitigating Controls (NIST 800-53 r5) AI
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Input validation directly stops data from being filtered or collapsed into unsafe values that violate security properties.
Output filtering enforces validation of data leaving the system so collapsed unsafe values are not produced.
Security engineering principles require safe data transformation and sanitization that prevent collapse into unsafe values.
Process isolation confines resource consumption to separate domains, reducing blast radius without stopping the root flaw.
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.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Secure SDLC practices directly require robust input validation and sanitization that prevent data-collapse flaws.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
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.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
Security testing can detect collapse issues but does not prevent them by itself.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.