1905 matches found
CVE-2026-10300 SGLang Inference HTTP Endpoint lora_manager.py assertion
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...
CVE-2026-10300
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...
CVE-2026-10300
SGLang 0.5.10.post1 contains a vulnerability in the Inference HTTP Endpoint, specifically in python/sglang/srt/lora/lora_manager.py where manipulation of the lora_path argument can trigger a reachable assertion. The issue is exposed over the network with high attack complexity and no authenticati...
CVE-2026-10300 SGLang Inference HTTP Endpoint lora_manager.py assertion
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...
CVE-2026-10300
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...
CVE-2026-10300 SGLang Inference HTTP Endpoint lora_manager.py assertion
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...
CVE-2026-49121 AI Tensor Engine for ROCm (AITER) 0.1.14 Unauthenticated RCE via MessageQueue.recv() Pickle Deserialization
AI Tensor Engine for ROCm AITER through 0.1.14 contains an unauthenticated remote code execution vulnerability in the MessageQueue.recv function within shmbroadcast.py that allows unauthenticated remote attackers to execute arbitrary code by sending a malicious pickle payload to a ZMQ SUB socket...
CVE-2026-49121
CVE-2026-49121 affects AI Tensor Engine for ROCm (AITER) up to version 0.1.14. The vulnerability exists in the MessageQueue.recv() function in shm_broadcast.py, where an unauthenticated remote attacker can deliver a crafted pickle payload to a ZMQ SUB socket (no authentication, no HMAC, no format...
EUVD-2026-33717
AI Tensor Engine for ROCm AITER through 0.1.14 contains an unauthenticated remote code execution vulnerability in the MessageQueue.recv function within shmbroadcast.py that allows unauthenticated remote attackers to execute arbitrary code by sending a malicious pickle payload to a ZMQ SUB socket...
The software for deploying and executing AI models, NVIDIA Triton Inference Server (previously TensorRT Inference Server), is vulnerable due to synchronization errors when using a shared resource. This vulnerability allows attackers to cause service failures.
The vulnerability of the software for deploying and executing AI models in NVIDIA Triton Inference Server previously known as TensorRT Inference Server stems from synchronization errors when using a shared resource. Exploiting this vulnerability can allow a malicious actor to cause service failur...
The vulnerability of the software for deploying and executing AI models with NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) involves uncontrolled memory consumption, which allows a malicious actor to cause service failures.
The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to uncontrolled memory consumption. Exploiting this vulnerability can allow a malicious actor, operating remotely, to trigger a service failure...
The software for deploying and executing AI models, NVIDIA Triton Inference Server (previously TensorRT Inference Server), is vulnerable due to synchronization errors when using a shared resource. This vulnerability allows attackers to cause service failures.
The vulnerability of the software for deploying and executing AI models in NVIDIA Triton Inference Server previously known as TensorRT Inference Server stems from synchronization errors when using a shared resource. Exploiting this vulnerability can allow a malicious actor to cause service failur...
SkillGuard: A Permission Framework for Agent Skills
Agent skills extend LLM agents with reusable instructions, scripts, tool bindings, and contextual dependencies. However, current skill ecosystems largely rely on trust-based loading and static inspection, leaving a gap between what a skill can inject into an agent's context and what it can cause...
PT-2026-45663
Name of the Vulnerable Software and Affected Versions SGLang version 0.5.10.post1 Description A flaw exists in the Inference HTTP Endpoint component within the file python/sglang/srt/lora/lora manager.py. Remote manipulation of the lora path argument can trigger a reachable assertion, potentially...
CVE-2026-10300: Reachable Assertion
A security vulnerability has been detected in SGLang 0.5.10.post1. Impacted is an unknown function of the file python/sglang/srt/lora/loramanager.py of the component Inference HTTP Endpoint. Such manipulation of the argument lorapath leads to reachable assertion. The attack can be launched...
sglang 安全漏洞
SGLang is a programming language and runtime system developed by SGL-project, aimed at accelerating large model inference. Version SGLang 0.5.10.post1 contains a security vulnerability. This vulnerability stems from an unknown function in the Inference HTTP Endpoint component file...
Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms
The need for secure and private Artificial Intelligence AI and Machine Learning ML on edge and mobile devices has increased the necessity of protecting the architecture of these systems from threats to both security and privacy. With an ever-increasing number of pre-trained AI models being used o...
swimtrack-poc
SwimTrack PoC This directory contains a proof of concept for...
Distributed AI Inference: Why Placement Is the New Bottleneck
In real AI systems, bottlenecks don't disappear, they move. Learn about why inference placement, not raw compute, is the decisive infrastructure question...
On Reliability of Efficient Membership Inference Vulnerability Evaluation
Membership inference attacks MIAs are popular methods for empirically assessing the leakage of sensitive information in the training data through models or statistics learned from the data. The MIA vulnerability is often evaluated through false positive rate FPR and true positive rate TPR of a...