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CVEs in KEV CVEs with EPSS >= 80% Crit. Microsoft High Apache SQL Injection (CWE-89) Linux Kernel High (CVSS 3.1) Apache Struts RCE (Remote Code Execution) XSS (CWE-79) Critical (CVSS 4.0) CVEs to check

Search Results (12 CVEs found)

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CVE Vendors Products Updated CVSS v3.1
CVE-2026-109226 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-10-13 7.5 High
SGLang through 0.5.21 contains a decompression amplification vulnerability in load_audio that allows remote attackers to exhaust memory by submitting highly compressed audio decoded without duration or sample limits. Attackers can supply small FLAC-encoded silence files through audio_url or audio_data inputs that expand into massive PCM buffers, exhausting worker memory and CPU for denial of service.
CVE-2026-109225 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-10-13 7.5 High
SGLang through 0.5.21 contains an unbounded memory allocation vulnerability that allows unauthenticated remote attackers to exhaust memory by uploading oversized files to /v1/audio/transcriptions. Attackers can send concurrent large multipart uploads, which are fully read into memory before validation, to crash the HTTP/tokenizer worker and deny service.
CVE-2026-109229 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-10-13 7.5 High
SGLang through 0.5.21 contains a denial of service vulnerability that disables Pillow decompression bomb protection process-wide by setting Image.MAX_IMAGE_PIXELS to None in NanoNemotronVLImageProcessor. Remote attackers can submit small, highly compressed images with huge dimensions to the generic image loader, exhausting worker memory during decoding.
CVE-2026-109227 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-10-12 7.5 High
SGLang through 0.5.21 contains an unbounded memory allocation vulnerability in preprocess_video in qwen_vl.py that decodes frames at full source resolution before applying max_pixels limits. Remote attackers can submit high-resolution videos to /v1/chat/completions on Qwen-VL deployments, and concurrent requests exhaust worker memory causing denial of service.
CVE-2026-109228 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-10-12 7.5 High
SGLang through 0.5.21 contains a resource budget bypass vulnerability that allows remote attackers to override GLM-V video processing limits by supplying fps, max_frames, max_tokens_per_frame and max_image_tokens in the video_url object. Attackers can set these values arbitrarily high so glm4v.py decodes every frame of high-resolution video at full size, exhausting worker memory and denying service.
CVE-2026-105245 1 Sgl-project 1 Sglang 2026-10-07 3.7 Low
A vulnerability has been found in sgl-project sglang up to 0.5.21. This issue affects the function server_info of the file python/sglang/srt/entrypoints/http_server.py of the component HTTP Endpoint. Such manipulation of the argument api_key leads to cleartext transmission of sensitive information. It is possible to launch the attack remotely. Attacks of this nature are highly complex. The exploitability is assessed as difficult. The exploit has been disclosed to the public and may be used. The pull request to fix this issue awaits acceptance.
CVE-2026-93688 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-09-21 7.5 High
SGLang through 0.5.19 in prefill/decode disaggregation mode with Mooncake KV transfer backend fails to validate bootstrap_room values, allowing unbounded transfer state allocation. Unauthenticated attackers can reach the decode engine's POST /generate endpoint and submit arbitrary bootstrap_room values to exhaust prefill process memory until out-of-memory termination.
CVE-2026-93838 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-09-20 5.9 Medium
SGLang versions through 0.5.20 contain an unbounded memory allocation vulnerability in handle_staging_req() that fails to validate chunk_idx from ZMQ STAGING_REQ frames in prefill/decode disaggregation deployments. Attackers with access to the decode engine's internal ZMQ rank port can send a frame with an extremely large chunk_idx value, causing the scheduler to allocate memory until the system runs out and terminates the process.
CVE-2026-92972 1 Sgl-project 1 Sglang 2026-09-17 8.6 High
SGLang through 0.5.19 in prefill/decode disaggregation mode contains an unauthenticated PUT /route endpoint on the prefill bootstrap service that allows attackers to poison the KV transfer routing table. Attackers can supply arbitrary rank_ip and rank_port values to redirect decode workers to attacker-controlled endpoints, causing denial of service or disclosure of KV transfer metadata including session identifiers and tensor-parallel topology parameters.
CVE-2026-10775 2 Lmsys, Sgl-project 2 Sglang, Sglang 2026-06-10 3.6 Low
A vulnerability was determined in sgl-project SGLang up to 0.5.11. Affected by this vulnerability is the function data_hash of the component Cache Handler. This manipulation causes denial of service. The attack is restricted to local execution. A high degree of complexity is needed for the attack. The exploitation appears to be difficult. The exploit has been publicly disclosed and may be utilized. The pull request to fix this issue awaits acceptance.
CVE-2026-10300 2 Sgl-project, Sglang 2 Sglang, Sglang 2026-06-02 3.7 Low
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/lora_manager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lora_path leads to reachable assertion. The attack can be launched remotely. A high complexity level is associated with this attack. The exploitability is considered difficult. The exploit has been disclosed publicly and may be used. The pull request to fix this issue awaits acceptance.
CVE-2026-7669 1 Sgl-project 1 Sglang 2026-05-05 5.6 Medium
A vulnerability was detected in sgl-project SGLang up to 0.5.9. Impacted is the function get_tokenizer of the file python/sglang/srt/utils/hf_transformers_utils.py of the component HuggingFace Transformer Handler. The manipulation of the argument trust_remote_code with the input False as part of Boolean results in code injection. The attack can be executed remotely. A high complexity level is associated with this attack. The exploitability is considered difficult. In get_tokenizer(), when the caller passes trust_remote_code=False and HuggingFace transformers v5 returns a TokenizersBackend instance (the generic fallback for tokenizer classes not in the registry), SGLang silently re-invokes AutoTokenizer.from_pretrained with trust_remote_code=True, overriding the caller's explicit security setting. A model repository containing a malicious tokenizer.py referenced via auto_map in tokenizer_config.json will execute arbitrary Python in the SGLang process during this second call. No log line or warning is emitted. The override affects all current SGLang versions because transformers==5.3.0 is pinned in pyproject.toml. Both tokenizer_mode="auto" and tokenizer_mode="slow" are affected. The exploit is now public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.