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Schwachstellenarchiv 100
Filter zurücksetzenvLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package.
vLLM: OpenAI auth bypass
vLLM introduced enhanced protection for CVE-2025-62164
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processin
vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters
vLLM makes Use of Uninitialized Resource
vLLM: Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server
vLLM: Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing
vLLM: Processing differential in multi-channel audio downmixing enables hidden-input/moderation bypass for audio models
vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out
vLLM is vulnerable to DoS in Idefics3 vision models via image payload with ambiguous dimensions
vLLM allows clients to crash the openai server with invalid regex
vLLM DOS: Remotely kill vllm over http with invalid JSON schema
vLLM has a Regular Expression Denial of Service (ReDoS, Exponential Complexity) Vulnerability in `pythonic_tool_parser.py`
Potential Timing Side-Channel Vulnerability in vLLM’s Chunk-Based Prefix Caching
vLLM has a Weakness in MultiModalHasher Image Hashing Implementation
vLLM vulnerable to Regular Expression Denial of Service
vLLM Vulnerable to Remote Code Execution via Mooncake Integration
CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0
vLLM vulnerable to Denial of Service by abusing xgrammar cache
xgrammar Vulnerable to Denial of Service (DoS) by abusing unbounded cache in memory
vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints
Erkennen Sie Schwachstellen in Ihrer Anwendung
Mit der DockRay-Integration und aktivierter Softwareinventarisierung prüft DockRay gemeldete Komponentenversionen auf bekannte Schwachstellen und benachrichtigt Sie über bestätigte Treffer. Die Abdeckung hängt von der unterstützten Technologie und den verfügbaren Meldungen ab.