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 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...

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...

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...

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...

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...

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...

The Semantic Trap: Do Fine-Tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern?

LLMs demonstrate promising performance in software vulnerability detection after fine-tuning. However, it remains unclear whether these gains reflect a genuine understanding of vulnerability root causes or merely an exploitation of functional patterns. In this paper, we identify a critical failur...

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world scenarios that can amplify backdoor threats. This paper presents the first in-depth investigation of how the dataset...

CVE-2026-22262

A flaw was found in Suricata, a network intrusion detection/prevention system IDS/IPS. When saving a dataset, the system uses a stack buffer to process the data. If an attacker provides excessively large data within a dataset, it can cause a stack overflow. This vulnerability could lead to a deni...

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...