CVE-2026-73555 is a medium-severity (CVSS 5.3) CWE-209 vulnerability in vllm. A fix is available for vllm — see the affected versions and patch details below.
vLLM: Unauthenticated Internal Path and Username Disclosure via Validation Error Messages
Exploitation Status
No confirmed exploitation observed yet
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-73555.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2026-73555 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 391,915 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
Real-World Exposure
vllmReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Summary
When the vLLM API receives a malformed request (e.g., invalid JSON or missing required fields), FastAPI raises a Pydantic RequestValidationError. The validation_exception_handler in vllm/entrypoints/openai/server_utils.py converts this exception to a string via str(exc), which includes the internal file path and line number of the handler function. The existing sanitize_message() function in vllm/entrypoints/utils.py strips memory addresses (e.g., 0x7f...) but does not strip File "...", line X patterns. The result is a user-facing HTTP response that leaks internal system information.
Impact
An unauthenticated attacker can extract the following with a single malformed request:
- OS username running the vLLM process (e.g.,
ubuntu) - Home directory path (e.g.,
/home/ubuntu/) - Virtual environment path (e.g.,
vllm-env/) - Python version (e.g.,
3.12) - Internal package structure and line numbers (e.g.,
vllm/entrypoints/openai/chat_completion/api_router.py) - Handler function names per endpoint, enabling precise version fingerprinting
This information aids attackers in constructing targeted exploits: environment paths narrow the attack surface, and handler function names + line numbers enable exact version identification even when the /version endpoint is disabled.
All POST endpoints that accept JSON bodies are affected, including /v1/chat/completions, /v1/completions, /tokenize, and /detokenize.
Workarounds
Deploying vLLM behind a reverse proxy that rewrites error response bodies to strip file paths would mitigate this, though it is fragile.
Remediation Recommendation
Two possible fixes (either suffices):
Option A — Fix validation_exception_handler: Construct the error message from exc.errors() (the structured Pydantic error list) rather than str(exc). This avoids the traceback-style string entirely.
Option B — Fix sanitize_message: Add a regex to strip File "...", line \d+ patterns, similar to how memory addresses are already stripped:
import re
msg = re.sub(r'File ".*?", line \d+, in \w+', '[internal]', msg)
Option A is preferred as it addresses the root cause rather than filtering symptoms.
Environment Tested
- vLLM 0.20.1 (pip install, latest stable as of May 2026)
- Python 3.12
- Ubuntu 22.04
- Model: Qwen/Qwen2-0.5B (text-only; bug is model-independent)
This was fixed here: https://github.com/vllm-project/vllm/commit/e87521626f
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | vllm | all versions | 0.26.0pip install --upgrade 'vllm==0.26.0' |
Affected Products
vllmvllmDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update vllm to 0.26.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-73555 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
Moderate: vLLM, as deployed in Red Hat AI Inference Server and Red Hat OpenShift AI, is susceptible to information disclosure. Unauthenticated, malformed JSON requests to its API endpoints can expose sensitive system details, including OS usernames, file paths, Python version, and internal package structure, due to…
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat AI Inference Server 3.5 | rhaii/vllm-rocm-rhel9:1791425869 | RHSA-2026:82059 |
| Red Hat AI Inference Server 3.5 | rhaii/vllm-gaudi-rhel9:1791427182 | RHSA-2026:82061 |
| Red Hat AI Inference Server 3.5 | rhaii/vllm-cuda-rhel9:1791473631 | RHSA-2026:82368 |
| Red Hat AI Inference Server 3.5 | rhaii/vllm-spyre-rhel9:1791818357 | RHSA-2026:82371 |
| Red Hat AI Inference Server 3.5 | rhaii/vllm-cpu-rhel9:1791473614 | RHSA-2026:82415 |
Frequently Asked Questions
Is CVE-2026-73555 in your dependencies?
Find it across PyPI, including transitive dependencies.