Medium: docker

Issue Overview: containerd is an open-source container runtime. A bug was found in containerd prior to versions 1.6.38, 1.7.27, and 2.0.4 where containers launched with a User set as a UID:GID larger than the maximum 32-bit signed integer can cause an overflow condition where the container...

PYSEC-2025-54

vLLM is an inference and serving engine for large language models LLMs. In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid jsonschema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex...

PYSEC-2025-55

vLLM is an inference and serving engine for large language models LLMs. Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service ReDoS that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to...

CVE-2025-48942

vLLM is an inference and serving engine for large language models LLMs. In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid jsonschema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex...

CVE-2025-48944 vLLM Tool Schema allows DoS via Malformed pattern and type Fields

vLLM is an inference and serving engine for large language models LLMs. In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the "pattern" and "type" fields when the tools functionality ...

PYSEC-2025-50

vLLM, an inference and serving engine for large language models LLMs, has a Regular Expression Denial of Service ReDoS vulnerability in the file vllm/entrypoints/openai/toolparsers/pythonictoolparser.py of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and...

UBUNTU-CVE-2020-36846

A buffer overflow, as described in CVE-2020-8927, exists in the embedded Brotli library. Versions of IO::Compress::Brotli prior to 0.007 included a version of the brotli library prior to version 1.0.8, where an attacker controlling the input length of a "one-shot" decompression request to a scrip...

PYSEC-2025-53

vLLM is an inference and serving engine for large language models LLMs. Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT Time to First Token. These timing differences...

CVE-2025-46570 vLLM’s Chunk-Based Prefix Caching Vulnerable to Potential Timing Side-Channel

vLLM is an inference and serving engine for large language models LLMs. Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT Time to First Token. These timing differences...

SUSE-SU-2025:20375-1 Security update for libsoup

This update for libsoup fixes the following issues: - CVE-2025-2784: Fixed Heap buffer over-read in skipinsignificantspace when sniffing content bsc1240750 - CVE-2025-32050: Fixed Integer overflow in appendparamquoted bsc1240752 - CVE-2025-32051: Fixed Segmentation fault when parsing malformed da...

Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models

Model merging for Large Language Models LLMs directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. However, due to potential vulnerabilities in models available on open-source platforms, model merging is susceptible to...

Medium: docker

Issue Overview: containerd is an open-source container runtime. A bug was found in containerd prior to versions 1.6.38, 1.7.27, and 2.0.4 where containers launched with a User set as a UID:GID larger than the maximum 32-bit signed integer can cause an overflow condition where the container...

libsoup: Denial of Service attack to websocket server

A flaw was found in libsoup. The SoupWebsocketConnection may accept a large WebSocket message, which may cause libsoup to allocate memory and lead to a denial of service DoS...

Smart Contracts for SMEs and Large Companies

Research on blockchains addresses multiple issues, with one being writing smart contracts. In our previous research we described methodology and a tool to generate, in automated fashion, smart contracts from BPMN models. The generated smart contracts provide support for multi-step transactions th...

unbound: Unbounded name compression could lead to Denial of Service

A flaw was found in Unbound which can lead to degraded performance and an eventual denial of service when handling replies with very large RRsets that require name compression to be applied. Versions prior to 1.21.1 do not have a hard limit on the number of name compression calculations that...

System Prompt Extraction Attacks and Defenses in Large Language Models

The system prompt in Large Language Models LLMs plays a pivotal role in guiding model behavior and response generation. Often containing private configuration details, user roles, and operational instructions, the system prompt has become an emerging attack target. Recent studies have shown that...

Red-Teaming Text-To-Image Systems by Rule-Based Preference Modeling

Text-to-image T2I models raise ethical and safety concerns due to their potential to generate inappropriate or harmful images. Evaluating these models' security through red-teaming is vital, yet white-box approaches are limited by their need for internal access, complicating their use with...

PandaGuard: Systematic Evaluation of LLM Safety against Jailbreaking Attacks

Large language models LLMs have achieved remarkable capabilities but remain vulnerable to adversarial prompts known as jailbreaks, which can bypass safety alignment and elicit harmful outputs. Despite growing efforts in LLM safety research, existing evaluations are often fragmented, focused on...

Phare: a Safety Probe for Large Language Models

Ensuring the safety of large language models LLMs is critical for responsible deployment, yet existing evaluations often prioritize performance over identifying failure modes. We introduce Phare, a multilingual diagnostic framework to probe and evaluate LLM behavior across three critical...

Semantic-Preserving Adversarial Attacks on LLMs: an Adaptive Greedy Binary Search Approach

Large Language Models LLMs increasingly rely on automatic prompt engineering in graphical user interfaces GUIs to refine user inputs and enhance response accuracy. However, the diversity of user requirements often leads to unintended misinterpretations, where automated optimizations distort...