vLLM
CVE-2025-59425
HIGH
Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessReach vLLM /v1 endpoint over network
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DeliverySend many timed Bearer-token probes
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ExploitMeasure response-time differences per character
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ExecutionRecover valid API key incrementally
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ImpactAuthenticate and abuse inference API
Vulnerability Assessment
| Weakness |
CWE
ASVS 5.0
V15 - Secure Coding and Architecture Covert timing channel
|
| Exploitation Conditions | Exploitable ONLY against deployments that enable vLLM's built-in API key authentication (the AuthenticationMiddleware / --api-key path guarding /v1 routes) in versions before 0.11.0rc2; deployments with no auth, or those fronted by an external API gateway/reverse proxy that handles authentication, are not affected by this specific flaw. No user interaction or prior authentication is required (PR:N, UI:N), but practical exploitation is LIMITED by the need for a very large number of timed requests and by network latency/jitter, which can swamp the per-character timing signal - making remote exploitation probabilistic and slow rather than deterministic. The attacker must also have network reachability to the vLLM API endpoint. |
| Impact | Signals are moderate and largely consistent. CVSS 3.1 (7.5, AV:N/AC:L/PR:N/UI:N/C:H/I:N/A:N) frames this as a network-reachable, unauthenticated confidentiality issue - the recovered API key grants full access to the LLM inference endpoint. However, the AC:L rating understates reality: a remote timing side-channel against a SHA-free string comparison requires large volumes of requests and is heavily degraded by network jitter, so practical exploitation is harder than AC:L implies (I assess AC:H). EPSS is low (0.57%, 46th percentile), and the vulnerability is NOT in CISA KEV, indicating no confirmed active exploitation. POC/exploit code is reported publicly available and the GitHub advisory (GHSA-wr9h-g72x-mwhm) plus the fix commit confirm the root cause, but publicly available exploit code is a distinct signal from active exploitation and should not be conflated with it. Net: this is a legitimate authentication-weakness to patch, but it is a high-CVSS / lower-real-world-risk item - exploitation is non-trivial, probabilistic, and only affects deployments that actually rely on vLLM's built-in API key auth rather than an external gateway. Note CWE-385 (Covert Timing Channel) matches the mechanism and corroborates the timing-attack framing. |
| Exploit ScenarioDerived | An attacker with network access to a vLLM server that uses its built-in --api-key authentication issues a large, automated series of Bearer-token requests, measuring response-time differences to recover the key one character at a time (the vulnerable byte-by-byte comparison takes longer as more leading characters match). Having reconstructed a valid key, the attacker authenticates and freely abuses the LLM inference API. Publicly available exploit code is reported for this technique, though success depends on low network jitter and many samples. |
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Alert meAnalysisAIv1 – Oct 19, 2026
Timing-based API key recovery in vLLM before 0.11.0rc2 can let remote attackers bypass bearer-token authentication on deployments that rely on vLLM's built-in API key validation for /v1 routes. The flaw is a non-constant-time string comparison in AuthenticationMiddleware; exploitation requires many carefully timed requests and is practical only when network latency and jitter are low enough not to mask the per-character signal, so the assessed severity is high confidentiality impact with high attack complexity (AC:H). Publicly available exploit code exists, EPSS is low at 0.57% (46th percentile), and there is no CISA KEV confirmation of active exploitation.
RemediationAI
Vendor-released patch: upgrade vLLM to 0.11.0rc2 or later (the 0.11.0 release line includes the fix), which replaces the vulnerable comparison with SHA-256 digests and secrets.compare_digest; advisory and fix details are at https://github.com/vllm-project/vllm/security/advisories/GHSA-wr9h-g72x-mwhm and https://github.com/vllm-project/vllm/commit/ee10d7e6ff5875386c7f136ce8b5f525c8fcef48. If upgrading is not immediately possible, place vLLM behind an API gateway or reverse proxy that enforces constant-time bearer-token validation and restrict network access to trusted clients, accepting extra latency and the need to correctly forward or strip Authorization headers; alternatively backport the upstream commit. Do not simply disable built-in API key authentication unless an external gateway already enforces authentication, because doing so exposes the /v1 endpoints. Monitor for bursts of 401 responses that could indicate timing probes.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 1 package in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
vLLM's OpenAI-compatible API server protects /v1 endpoints with AuthenticationMiddleware when --api-key is configured. In affected builds the middleware compares the incoming Authorization header against precomputed 'Bearer {token}' strings with ordinary equality, so comparison time grows with the number of matching leading characters; this is a covert timing channel (CWE-385) that leaks information about the secret token across repeated requests. The upstream fix (commit ee10d7e6) parses the Bearer scheme, hashes the supplied token with SHA-256, and compares digests with secrets.compare_digest across configured tokens, making the check constant-time and avoiding direct comparison of secret material. CPE data identifies vLLM releases before 0.11.0rc2, including 0.11.0rc1, as affected; the independent assessment rates the attack as AC:H because remote timing measurements must overcome network latency and jitter.
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Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
Remote and unauthenticated (AV:N/PR:N), but AC:H because a timing side-channel over the network requires many samples and is degraded by jitter; recovered key exposes the API so C:H, with no direct integrity or availability impact.
Primary rating from Vendor (github).
CVSS VectorVendor: github
DescriptionCVE.org
vLLM is an inference and serving engine for large language models (LLMs). Before version 0.11.0rc2, the API key support in vLLM performs validation using a method that was vulnerable to a timing attack. API key validation uses a string comparison that takes longer the more characters the provided API key gets correct. Data analysis across many attempts could allow an attacker to determine when it finds the next correct character in the key sequence. Deployments relying on vLLM's built-in API key validation are vulnerable to authentication bypass using this technique. Version 0.11.0rc2 fixes the issue.
Blast Radius
ecosystem impact- 4 pypi packages depend on vllm (4 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.11.0.
Executive ViewAI
vLLM deployments prior to 0.11.0rc2 that rely on built-in API key validation for /v1 routes are exposed to a timing-based authentication bypass, potentially allowing unauthorized access to model endpoints and sensitive inference data. Publicly available exploit code exists, but there is no CISA KEV confirmation of active exploitation; high attack complexity and low EPSS reduce immediate likelihood, yet leadership should prioritize remediation because exposed AI services can lead to data disclosure and compute abuse.
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Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Authentication bypass attacks exploit flaws in the verification mechanisms that control access to systems and applications.
Affected Products (CPE)CVE.org
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Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th
vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where incons
Out-of-memory worker crashes in vLLM can be induced by a single small compressed audio payload submitted to the /v1/chat
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5),
GPU memory exhaustion in vLLM versions prior to 0.28.0 lets an attacker with low-privileged API access (PR:L in the CVSS
Multimodal inference requests to vLLM deployments running versions prior to 0.28.0 can trigger a process-level assertion
Uncontrolled resource consumption in vLLM's OpenAI-compatible completions endpoint allows any authenticated API client t
Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).
Race condition in vLLM's prompt embedding loader allows concurrent API requests to bypass the sparse tensor invariant gu
NVD
CVE.org
EPSS
GitHub
Exploit-DB