Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessSend malformed JSON body to /v1/chat/completions
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DeliveryFastAPI raises RequestValidationError with injected endpoint file context
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Exploitstr(exc) serializes full file path and handler name
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ExecutionUnpatched sanitize_message passes filesystem paths through
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ImpactHTTP 422 response body exposes server directory layout and Python version
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A10:2025 - Mishandling of Exceptional Conditions
ASVS 5.0
V16 - Security Logging and Error Handling Error message sanitization
MITRE ATT&CK
1 technique across 1 tactic: Discovery The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Exploitation Conditions | Exploitation requires that the target is running vLLM prior to version 0.26.0 with the OpenAI-compatible API server active, which is the default serving mode ( |
| Impact | The CVSS 5.3 vector (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N) accurately captures the network-accessible, zero-auth, information-disclosure-only nature of this flaw with no integrity or availability impact. However, the low complexity and zero-privilege requirements mean that exploitation is trivial against any internet-exposed vLLM instance - a single malformed JSON request suffices. No EPSS score was provided in the input data; exploitation probability cannot be quantified from available signals beyond the CVSS vector. There is no CISA KEV listing, and no standalone POC exploit has been identified - the public PR diff itself illustrates the vulnerability but is not packaged as an exploit tool. The disclosed data (filesystem layout, Python version, package paths, handler names) has meaningful reconnaissance value: it enables targeted path traversal attempts, dependency confusion attacks, or version-specific exploit chaining. For publicly exposed vLLM inference servers - a common deployment pattern for shared inference infrastructure - this represents a realistic, low-effort first-stage reconnaissance capability with no false negatives. |
| Exploit ScenarioDerived | An unauthenticated attacker sends a single HTTP POST request to |
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Alert meAnalysisAIv1 – Aug 13, 2026
Information disclosure in vLLM prior to version 0.26.0 leaks server filesystem internals - including OS usernames, home and virtual-environment directory paths, Python version, internal package structure, line numbers, and endpoint handler names - through HTTP error responses. Any unauthenticated network attacker can trigger this by sending malformed JSON to the OpenAI-compatible API endpoints, requiring zero privileges and no special configuration beyond a default vLLM deployment. No public exploit code has been identified at time of analysis, and no CISA KEV listing exists; however, the trivial exploitation bar (a single malformed HTTP request) and the reconnaissance value of the leaked data elevate real-world risk above the CVSS 5.3 score alone implies.
RemediationAI
Upgrade vLLM to version 0.26.0 or later, which resolves both underlying deficiencies: the validation_exception_handler in server_utils.py now constructs error messages from exc.errors() instead of str(exc), eliminating the FastAPI endpoint context leak, and sanitize_message in api_utils.py has been hardened with additional regex patterns to strip traceback frames and absolute filesystem paths. The fix is documented in the GitHub Security Advisory GHSA-hwrm-c4cx-rf4j, PR #46415, commit e87521626febe2763f997691d1599de4175f4324, and the v0.26.0 release at https://github.com/vllm-project/vllm/releases/tag/v0.26.0. If an immediate upgrade is not feasible, restrict network access to the vLLM API port using firewall rules or a reverse proxy that requires authentication before forwarding requests to the vLLM backend - this prevents unauthenticated external access to the affected endpoints but does not remediate the underlying application-layer flaw and provides no protection against authenticated or internal-network attackers.
Affected VersionsENISA
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 implements an OpenAI-compatible REST API layer using FastAPI and Python. The root cause is CWE-209 (Generation of Error Message Containing Sensitive Information), arising from two compounding deficiencies. First, validation_exception_handler in vllm/entrypoints/openai/server_utils.py serialized FastAPI RequestValidationError objects via str(exc), which includes endpoint context attributes (file path, line number, handler function name) that FastAPI injects during routing - not during construction - making them invisible to naive sanitization. Second, sanitize_message in vllm/entrypoints/utils.py only stripped memory addresses (hex 0x... patterns) but lacked regex coverage for traceback-style File "...", line N, in func frames and absolute filesystem paths. The fix in v0.26.0 (PR #46415, commit e87521626febe2763f997691d1599de4175f4324) addresses both: error messages are now built from exc.errors() directly rather than str(exc), and sanitize_message gained additional regex passes to strip traceback frames and common absolute path prefixes (/home, /usr, /opt, /var, /tmp, /root, /lib, /mnt, /srv). CPE cpe:2.3:a:vllm-project:vllm:*:*:*:*:*:*:*:* covers all pre-0.26.0 releases.
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Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
Default network-reachable API endpoint requires no authentication; confidentiality impact limited to path and version metadata disclosure with no integrity or availability consequence.
Primary rating from Vendor (GitHub_M).
CVSS VectorVendor: GitHub_M
Lifecycle Timeline
1DescriptionCVE.org
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the validation_exception_handler in vllm/entrypoints/openai/server_utils.py converts FastAPI RequestValidationError objects with str(exc), and sanitize_message in vllm/entrypoints/utils.py does not remove traceback-style file paths, allowing unauthenticated malformed JSON requests to /v1/chat/completions, /v1/completions, /tokenize, and /detokenize to disclose the OS username, home and virtual-environment paths, Python version, internal package structure, line numbers, and endpoint handler names. This issue is fixed in version 0.26.0.
Blast Radius
ecosystem impact- 2 pypi packages depend on vllm (2 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.26.0.
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
Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 10.0
vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. Rated critical sev
Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal
Timing-based API key recovery in vLLM before 0.11.0rc2 can let remote attackers bypass bearer-token authentication on de
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th
Denial of service in the vLLM LLM inference server (all versions prior to 0.24.0) allows a remote client to crash the sh
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
EUVD-2026-58053
GHSA-hwrm-c4cx-rf4j
NVD
CVE.org
EPSS
Vulnrichment
VulDB
Exploit-DB