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...

EUVD-2026-31493

The vllm-metal inference backend in Docker Model Runner on macOS unconditionally sets trustremotecode=True when loading model tokenizers, and runs without sandboxing. This causes transformers.AutoTokenizer.frompretrained to import and execute arbitrary Python files included in any model pulled fr...

CVE-2026-5817

CVE-2026-5817 affects the vllm-metal backend used by Docker Model Runner on macOS. The backend loads model tokenizers with trust_remote_code=True, causing transformers.AutoTokenizer.from_pretrained() to import and execute arbitrary Python files from models pulled from an OCI registry. This can en...

AI Security Research Should Better Incentivize Defense Research

This work examines an imbalance in artificial intelligence AI security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech...

Docker Desktop < 4.71.0 Container Escape (CVE-2026-5843)

The version of Docker Desktop installed on the remote host is prior to 4.71.0. It is, therefore, affected by a container escape vulnerability: - A container-to-host code execution vulnerability exists in the Docker Model Runner MLX inference backend. An attacker with access to a container could...

GHSA-RQ6V-X3J8-7QGF Amazon SageMaker Python SDK is missing integrity verification in its Triton inference handler

Summary Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker. An issue exists where, under certain circumstances, the Triton inference handler deserializes model artifacts without performing integrity verification, allowing...

Encrypted Neural Networks without Overflows

Fully homomorphic encryption FHE enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computation to a third party server without ever revealing the user's data. Currently, the CKKS scheme is the backbone of most efficient FHE implementations...

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications

Large Language Models LLMs have become the predominant paradigm in NLP, advancing both research and industry. As model sizes and pretraining data grow, concerns about Pretraining Data Exposure PDE increase due to the scale and opacity of training datasets. PDE refers to determining whether specif...

Important: Red Hat Security Advisory: Red Hat AI Inference Server 3.2.2 (ROCm)

Red Hat AI Inference Server 3.2.2 ROCm is now available. Red Hat® AI Inference Server...

Important: Red Hat Security Advisory: Red Hat AI Inference Server 3.2.2 (CUDA)

Red Hat AI Inference Server 3.2.2 CUDA is now available. Red Hat® AI Inference Server...

Astra Linux – Vulnerability in Firefox

While the text displayed in Autofill tooltips cannot be directly read by JavaScript, the text was rendered using page fonts. Side-channel attacks on the text by using specially crafted fonts could have led to this text being interpreted by the webpage. This vulnerability affects Firefox versions...

CVE-2026-24215

NVIDIA Triton Inference Server contains a vulnerability in the DALI backend, where an attacker could cause uncontrolled resource consumption. A successful exploit of this vulnerability might lead to denial of service...

CVE-2026-24209

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a path traversal issue. A successful exploit of this vulnerability might lead to denial of service...

CVE-2026-24210

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause an integer overflow. A successful exploit of this vulnerability might lead to denial of service...

CVE-2026-24208

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a path traversal issue. A successful exploit of this vulnerability might lead to denial of service...

CVE-2026-24206

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause an authentication bypass. A successful exploit of this vulnerability might lead to escalation of privileges, denial of service, or information disclosure...

CVE-2026-24207

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause an authentication bypass. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, denial of service, or information disclosure...

CVE-2026-24215

NVIDIA Triton Inference Server contains a vulnerability in the DALI backend, where an attacker could cause uncontrolled resource consumption. A successful exploit of this vulnerability might lead to denial of service...