Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-25293 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability in Omniauth Omniauth Saml. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique OS Exhaustion Flood (T1499.001); ranked in the top 29% of CVEs by exploit likelihood; 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 SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — 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.
ruby-saml is a Ruby library that implements SAML-based single sign-on. Prior to versions 1.12.4 and 1.18.0, the library decompresses incoming SAML responses using zlib without first validating the size of the inflated content. Because the message-size check occurs on the compressed data, an attacker can supply a small but highly compressible assertion that expands to a much larger payload after decompression, triggering excessive memory or CPU consumption.
An unauthenticated remote attacker who can reach a SAML endpoint can send a crafted compressed response to induce a denial of service. The vulnerability carries a CVSS 4.0 score of 7.7 with network attack vector, no required privileges or user interaction, and high impact on availability.
Public advisories, including GitLab’s March 2025 patch release and the ruby-saml project’s own release notes, direct users to upgrade to 1.12.4 or 1.18.0. The fixes add a post-inflation size check and are referenced in the corresponding GitHub commits.
The associated EPSS score has remained low, moving only from 0.0622 to a peak of 0.0670.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6413
Vulnerability Data
ruby-saml provides security assertion markup language (SAML) single sign-on (SSO) for Ruby. Prior to versions 1.12.4 and 1.18.0, ruby-saml is susceptible to remote Denial of Service (DoS) with compressed SAML responses. ruby-saml uses zlib to decompress SAML responses in case…
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they're compressed. It is possible to bypass the message size check with a compressed assertion since the message size is checked before inflation and not after. This issue may lead to remote Denial of Service (DoS). Versions 1.12.4 and 1.18.0 fix the issue.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
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.
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.
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.
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.
Early notification of anomalous resource consumption or system malfunctions enables throttling or isolation before availability is lost.