vLLM has Remote DoS via Invalid Recovered Token Reinjection

## Summary A frontend-legal multi-request speculative workload can make vLLM produce an out-of-vocabulary recovered token equal to `vocab_size`, convert that value to `-1` when choosing the next live token for a request, and then feed that `-1` back into the next drafter input ids. On Qwen3 GPTQ this reaches the worker-side drafting / attention path and crashes the engine with a GPU `device-side assert`. The same issue is reachable through the public gRPC request surface by sending a specific

vLLM: OOM Denial of Service via Audio Decompression Bomb

### Summary vLLM's `/v1/audio/transcriptions` endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. Tested on vLLM v0.19.0. ### Details `SpeechToTextProcessor` rejects uploads over `VLLM_MAX_AUDIO_CLIP_FILESIZE_MB` (default 25MB) based on compressed byte length, but the audio decoder in `audio.py` accumulates all decoded frames into memory with no size limit before returning: ```python # speech_to_text.py L184-189

vLLM: incomplete CVE-2026-22778 fix leaks PIL repr addresses via Anthropic router

# vLLM: incomplete CVE-2026-22778 fix leaks PIL repr addresses via the Anthropic API router **Researcher:** Kai Aizen — SnailSploit (@SnailSploit), Adversarial & Offensive Security Research **Severity:** CVSS 3.1 5.3 (Medium) `AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N` **Target:** https://github.com/vllm-project/vllm --- ## Summary The fix for CVE-2026-22778 / GHSA-4r2x-xpjr-7cvv (PRs #31987 and #32319) introduced `sanitize_message` and applied it at four FastAPI exception-handling sites in the O

vLLM: GGUF dequantize kernel int truncation exposes uninitialized GPU memory in multi-tenant serving

## Summary Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (`csrc/quantization/gguf/gguf_kernel.cu`) causes partial tensor processing. The output tensor is allocated at full size via `torch::empty` (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor d

vLLM: image EXIF Rotation & PNG tRNS Transparency Not Normalized, Causing Mismatch Between Model Input and Expectations

## Summary Issue 1: EXIF orientation not normalized → The image orientation processed by the model differs from how humans view it, introducing interpretation bias. Issue 2: PNG tRNS not explicitly flattened before converting to RGB → After conversion, transparent/semi-transparent pixels are rendered unexpectedly, making otherwise subtle overlay elements visible and distorting the input content. (This attack is similar to AlphaDog: RGBA handling is already correct in vLLM, but since tRNS permi