Lightweight Cluster-Based Federated Learning for Intrusion Detection in Heterogeneous IoT Networks

The rise of heterogeneous Internet of Things IoT devices has raised security concerns due to their vulnerability to cyberattacks. Intrusion Detection Systems IDS are crucial in addressing these threats. Federated Learning FL offers a privacy-preserving solution, but IoT heterogeneity and limited...

MalTool: Malicious Tool Attacks on LLM Agents

In a malicious tool attack, an attacker uploads a malicious tool to a distribution platform; once a user installs the tool and the LLM agent selects it during task execution, the tool can compromise the user's security and privacy. Prior work primarily focuses on manipulating tool names and...

GitLab 18.4 < 18.5.5 / 18.6 < 18.6.3 / 18.7 < 18.7.1 (CVE-2025-13772)

The version of GitLab installed on the remote host is affected by a vulnerability, as follows: - GitLab has remediated an issue in GitLab EE affecting all versions from 18.4 before 18.5.5, 18.6 before 18.6.3, and 18.7 before 18.7.1 that could have allowed an authenticated user to access and utili...

CVE-2026-1669

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

DEBIAN-CVE-2026-1669

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

AZL-77414 CVE-2026-1669 affecting package keras for versions less than 3.3.3-7

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

UBUNTU-CVE-2026-1669

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

CVE-2026-1669

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

CVE-2026-1669

CVE-2026-1669 describes an arbitrary file read in the Keras model loading path via HDF5 external dataset references. Affected versions are Keras 3.0.0 through 3.13.1 on all supported platforms. The vulnerability arises in the HDF5 integration used during model loading, enabling a remote attacker ...

CVE-2026-1669 Arbitrary File Read in Keras via HDF5 External Datasets

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

CVE-2026-1669 Arbitrary File Read in Keras via HDF5 External Datasets

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

CVE-2026-1669

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

CVE-2026-1669 Arbitrary File Read in Keras via HDF5 External Datasets

Arbitrary file read in the model loading mechanism HDF5 integration in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references...

CVE-2026-1669

Removed by vendor...

CVE-2026-26013

A flaw was found in LangChain. The ChatOpenAI.getnumtokensfrommessages method fetches arbitrary imageurl values without validation when computing token counts for vision-enabled models. This issue allows an attacker to cause Server-Side Request Forgery SSRF by providing malicious image URLs in us...

autopentest-ai

AutoPentest Automated web application penetration testing p...

Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

The rapid development of the AI agent communication protocols, including the Model Context Protocol MCP, Agent2Agent A2A, Agora, and Agent Network Protocol ANP, is reshaping how AI agents communicate with tools, services, and each other. While these protocols support scalable multi-agent...

PT-2026-7728

Name of the Vulnerable Software and Affected Versions Keras versions 3.0.0 through 3.13.1 Description A flaw exists in the model loading mechanism, specifically within the HDF5 integration of Keras. This issue allows a remote attacker to read local files and potentially disclose sensitive...

VulReaD: Knowledge-Graph-Guided Software Vulnerability Reasoning and Detection

Software vulnerability detection SVD is a critical challenge in modern systems. Large language models LLMs offer natural-language explanations alongside predictions, but most work focuses on binary evaluation, and explanations often lack semantic consistency with Common Weakness Enumeration CWE...

Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models

Jailbreaking large language models LLMs has emerged as a critical security challenge with the widespread deployment of conversational AI systems. Adversarial users exploit these models through carefully crafted prompts to elicit restricted or unsafe outputs, a phenomenon commonly referred to as...