1011 matches found
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...
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...
Linux Distros Unpatched Vulnerability : CVE-2026-1669
The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - 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 t...
AZL-77414 CVE-2026-1669 affecting package keras 3.3.3-6
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...
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 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...
LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection
Feature selection FS remains essential for building accurate and interpretable detection models, particularly in high-dimensional malware datasets. Conventional FS methods such as Extra Trees, Variance Threshold, Tree-based models, Chi-Squared tests, ANOVA, Random Selection, and Sequential...
CIC-Trap4Phish: A Unified Multi-Format Dataset for Phishing and Quishing Attachment Detection
Phishing attacks represents one of the primary attack methods which is used by cyber attackers. In many cases, attackers use deceptive emails along with malicious attachments to trick users into giving away sensitive information or installing malware while compromising entire systems. The...
CVE-2026-2107
A vulnerability was found in yeqifu warehouse up to aaf29962ba407d22d991781de28796ee7b4670e4. This affects the function loadAllLoginfo/deleteLoginfo/batchDeleteLoginfo of the file dataset\repos\warehouse\src\main\java\com\yeqifu\sys\controller\LoginfoController.java of the component Log Info...
Evasion of IoT Malware Detection Via Dummy Code Injection
The Internet of Things IoT has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has...
KRONE: Hierarchical and Modular Log Anomaly Detection
Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure is lost when they are stored as flat sequences. As a result, state-of-the-art methods risk missing true dependencies...
Deep Learning for Contextualized NetFlow-Based Network Intrusion Detection: Methods, Data, Evaluation and Deployment
Network Intrusion Detection Systems NIDS have progressively shifted from signature-based techniques toward machine learning and, more recently, deep learning methods. Meanwhile, the widespread adoption of encryption has reduced payload visibility, weakening inspection pipelines that depend on...
Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agent
Understanding TTPs Tactics, Techniques, and Procedures in malware binaries is essential for security analysis and threat intelligence, yet remains challenging in practice. Real-world malware binaries are typically stripped of symbols, contain large numbers of functions, and distribute malicious...
cosmos-predict2 (>=1.0.6 <=1.0.9), frankenstein-model (>=5.1.6 <=5.3.9) +11 more potentially affected by CVE-2026-24149 via megatron-core (>=0.10.0 <=0.13.1)
megatron-core PYPI version =0.10.0, =1.0.6, =5.1.6, =0.4.0, =1.0.0, =2.0.8, =2.0.8, =1.0.0, =1.0.0, =1.0.0, =1.0.0, =1.0.0, =1.0.0, =2.0.5, =5.0.4 Source cves: CVE-2026-24149 Source advisory: SNYK:PYTHON-MEGATRONCORE-15248398...
Reading between the Code Lines: On the Use of Self-Admitted Technical Debt for Security Analysis
Static Analysis Tools SATs are central to security engineering activities, as they enable early identification of code weaknesses without requiring execution. However, their effectiveness is often limited by high false-positive rates and incomplete coverage of vulnerability classes. At the same...
Badges, Bytes and Blackmail
Behind the scenes of law enforcement in cyber: what do we know about caught cybercriminals? What brought them in, where do they come from and what was their function in the crimescape? Introduction: One view on the scattered fight against cybercrime The growing sophistication and diversification ...
SUSE CVE-2026-22262
Suricata is a network IDS, IPS and NSM engine. While saving a dataset a stack buffer is used to prepare the data. Prior to versions 8.0.3 and 7.0.14, if the data in the dataset is too large, this can result in a stack overflow. Versions 8.0.3 and 7.0.14 contain a patch. As a workaround, do not us...