GitLab Advisory Database (GLAD)

When a user-configured proxy on webpack-dev-server has a broad context (e.g. /) and ws: true, it also intercepts the dev server's own HMR WebSocket and forwards it to the proxy target. This leaks the browser's cookies and Origin header to the backend, bypasses the dev server's Host/Origin validation, and corrupts the HMR socket (both HMR and the proxy end up writing to the same socket).

All temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. Note: -Infinity is correctly caught.

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.

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 OpenAI router. The sanitizer strips object-repr memory addresses (<_io.BytesIO object at 0x7a95e299e750> → <_io.BytesIO object>) before error messages reach the client, defeating the ASLR-bypass primitive that CVE-2026-22778 chained with a libopenjp2 heap overflow for RCE. The fix is incomplete: response paths added to vLLM at or after the …

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 permits …

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 data from other users' inference requests, …

There is a high severity vulnerability in Traefik's Kubernetes Gateway provider affecting the crossProviderNamespaces allowlist. For HTTPRoute rules that declare multiple (WRR) backendRefs, Traefik evaluates the allowlist against the target backendRef.namespace instead of the route's own namespace. As a result, an HTTPRoute created in a namespace that is not allow-listed can reference a cross-provider TraefikService such as api@internal, dashboard@internal or rest@internal by pointing backendRef.namespace at an allow-listed namespace covered by …

There is a high severity vulnerability in Traefik's Kubernetes Gateway provider affecting the crossProviderNamespaces allowlist. For HTTPRoute rules that declare multiple (WRR) backendRefs, Traefik evaluates the allowlist against the target backendRef.namespace instead of the route's own namespace. As a result, an HTTPRoute created in a namespace that is not allow-listed can reference a cross-provider TraefikService such as api@internal, dashboard@internal or rest@internal by pointing backendRef.namespace at an allow-listed namespace covered by …

Insufficient clearing of the output buffer in Java-based decompressor implementations in lz4-java 1.10.0 and earlier allows remote attackers to read previous buffer contents via crafted compressed input. In applications where the output buffer is reused without being cleared, this may lead to disclosure of sensitive data. JNI-based implementations are not affected.

Insufficient clearing of the output buffer in Java-based decompressor implementations in lz4-java 1.10.0 and earlier allows remote attackers to read previous buffer contents via crafted compressed input. In applications where the output buffer is reused without being cleared, this may lead to disclosure of sensitive data. JNI-based implementations are not affected.

Insufficient clearing of the output buffer in Java-based decompressor implementations in lz4-java 1.10.0 and earlier allows remote attackers to read previous buffer contents via crafted compressed input. In applications where the output buffer is reused without being cleared, this may lead to disclosure of sensitive data. JNI-based implementations are not affected.

This advisory has been marked as a false positive.

This advisory has been marked as a false positive.

This advisory has been marked as a false positive.

This advisory has been marked as False Positive and has been removed.

After an API Token exposure from an exploited trivy dependency, two new releases of litellm were uploaded to PyPI containing automatically activated malware, harvesting sensitive credentials and files, and exfiltrating to a remote API. Anyone who has installed and run the project should assume any credentials available to litellm environment may have been exposed, and revoke/rotate thema ccordingly.