Security Vulnerabilities
Open VSX Registry does not sanitize SVG files uploaded as extension icons prior to storage, and serves them with Content-Type: image/svg+xml without security headers such as Content-Security-Policy or Content-Disposition: attachment. This allows an attacker to publish an extension with a malicious SVG icon and achieve stored cross-site scripting (XSS) when a user navigates directly to the icon URL.
On deployments using local storage, script execution occurs within the Open VSX application origin, enabling session hijacking, authentication token theft, and unauthorized extension publishing. On deployments backed by external storage (such as open-vsx.org with an S3-backed CDN), execution is confined to the storage origin, reducing impact but still permitting phishing attacks and credential harvesting through attacker-crafted pages.
CVSS Score
4.1
EPSS Score
0.002
Published
2026-06-23
A flaw was found in OpenSSH. This vulnerability, a heap out-of-bounds read, occurs during the cleanup of GSSAPI (Generic Security Service Application Programming Interface) indicators when a trailing NULL termination is missing in the auth-indicators array. A remote attacker, under specific configurations involving GSSAPI authentication and a Kerberos environment, could exploit this to cause the SSH authentication path to crash or abort. This leads to a denial of service (DoS), impacting the availability of the SSH service.
CVSS Score
3.7
EPSS Score
0.003
Published
2026-06-23
A flaw was found in OpenSSH. A local unprivileged attacker on a Linux client host can hijack client-side X11 forwarding connections. This is possible by pre-binding the preferred abstract X socket name when X11 forwarding is enabled and a local UNIX-domain X socket is used. A successful attack can compromise the confidentiality of forwarded X11 traffic, including sensitive window contents and input, and may allow some manipulation of the forwarded session.
CVSS Score
5.0
EPSS Score
0.001
Published
2026-06-23
A flaw was found in OpenSSH. A malicious SSH server can exploit a double free vulnerability in the Diffie-Hellman Group Exchange (DH-GEX) client path. This occurs during FIPS (Federal Information Processing Standards) mode known-group validation when the client processes attacker-controlled DH-GEX group parameters. Successful exploitation leads to client-side process termination, resulting in a Denial of Service (DoS).
CVSS Score
4.3
EPSS Score
0.002
Published
2026-06-23
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, 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. This vulnerability is fixed in 0.23.1rc0.
CVSS Score
6.5
EPSS Score
0.002
Published
2026-06-22
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll 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. This vulnerability is fixed in 0.23.1rc0.
CVSS Score
6.9
EPSS Score
0.003
Published
2026-06-22
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.
CVSS Score
5.3
EPSS Score
0.008
Published
2026-06-22
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0.
CVSS Score
6.5
EPSS Score
0.001
Published
2026-06-22
vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, 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, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.
CVSS Score
5.3
EPSS Score
0.003
Published
2026-06-22
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. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.
CVSS Score
8.8
EPSS Score
0.003
Published
2026-06-22