Noisy Networks, Nosy Neighbors: Simple Privacy Attacks against Residential Wireless Traffic

Smart devices, such as light bulbs, TVs, fridges, etc., equipped with computing capabilities and wireless communication, are part of everyday life in many households. Previous work has already shown that a passive eavesdropper can derive private information, household routines, etc., from the...

PT-2026-37143

Name of the Vulnerable Software and Affected Versions Admidio versions prior to 5.0.9 Description The member assignment DataTables endpoint 'members assignment data.php' includes hidden profile fields in its SQL search condition regardless of visibility settings. While the JSON output suppresses...

EDySec: A Deep Learning-Based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning ML detectors struggle to detect these...

SecScan

SecScan Local-LLM-powered security scanner for GitHub repos...

CVE-2026-25874

LeRobot through 0.5.1 contains an unsafe deserialization vulnerability in the async inference pipeline where pickle.loads is used to deserialize data received over unauthenticated gRPC channels without TLS in the policy server and robot client components. An unauthenticated network-reachable...

ONE Input Validation Error Vulnerability

ONE is a high-performance edge-side neural network inference framework developed by Samsung. Versions prior to ONE 1.30.0 contained a vulnerability related to input validation errors. This vulnerability stemmed from integer overflows during the calculation of the size of constant tensor data, whi...

API Security Based on Automatic OpenAPI Mapping

This paper presents Map Reduce Graph MRG, a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and...

GuardPhish: Securing Open-Source LLMs from Phishing Abuse

The rapid adoption of open-source Large Language Models LLMs in offline and enterprise environments has introduced a largely unexamined security risk like susceptibility to adversarial phishing prompts under static safety configurations. In this work, we systematically investigate this...

Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.3.1 (CUDA)

Red Hat AI Inference Server Model Optimization Tools 3.3.1 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...

Important: Red Hat Security Advisory: Red Hat AI Inference Server 3.3.1 (CUDA)

Red Hat AI Inference Server 3.3.1 CUDA is now available. Red Hat® AI Inference Server...

Important: Red Hat Security Advisory: Red Hat AI Inference Server 3.3.1 (ROCm)

Red Hat AI Inference Server 3.3.1 ROCm is now available. Red Hat® AI Inference Server...

Evaluating Differential Privacy against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

While Federated Learning FL mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks MIAs. This paper presents an empirical evaluation of Differential Privacy DP as a defense mechanism against MIAs in FL, leveraging the environment of the 202...

mlopspython-inference (>=67580760626172675.0.0, <=757069802391466481559610331145) potentially affected by CVE-2026-1462 via keras (2.12.0rc0)

keras PYPI version 2.12.0rc0 is affected by a known vulnerability. The following packages have a transitive dependency on keras and may be impacted: - mlopspython-inference =67580760626172675.0.0, =757069802391466481559610331145 Source cves: CVE-2026-1462 Source advisory: OSV:GHSA-4F3F-G24H-FR8M...

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...

NVIDIA Triton Inference Server Type Conversion DoS

The vulnerability of NVIDIA Triton Inference Server’s deployment and AI model execution software is related to incorrect conversion of numerical value types. Exploiting this vulnerability can allow a remote attacker to cause service failures...

CVE-2026-24174

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a server crash by sending a malformed request to the server. A successful exploit of this vulnerability might lead to denial of service...

NVIDIA Triton Inference Server DoS

The vulnerability of NVIDIA Triton Inference Server’s deployment and AI model execution software is related to an error in handling exceptional states. Exploiting this vulnerability can allow a remote attacker to cause service failures...

EUVD-2026-19757

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a server crash by sending a malformed request to the server. A successful exploit of this vulnerability might lead to denial of service...

EUVD-2026-19759

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a server crash by sending a malformed request header to the server. A successful exploit of this vulnerability might lead to denial of service...

EUVD-2026-19751

NVIDIA Triton Inference Server contains a vulnerability in triton server where an attacker may cause an information disclosure by uploading a model configuration. A successful exploit of this vulnerability may lead to information disclosure or denial of service...