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...

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...

Next-Generation Cyberattack Detection with Large Language Models: Anomaly Analysis across Heterogeneous Logs

This project explores large language models LLMs for anomaly detection across heterogeneous log sources. Traditional intrusion detection systems suffer from high false positive rates, semantic blindness, and data scarcity, as logs are inherently sensitive, making clean datasets rare. We address...

Evaluating and Enhancing the Vulnerability Reasoning Capabilities of Large Language Models

Large Language Models LLMs have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists: models frequently yield correct detection verdicts based on hallucinated logic or superficial patterns that deviate from the actual root cause. This...

Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection

Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches still operate at the function level, where models are asked to predict whether a single function is vulnerable without...

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...

PIDSMaker: Building and Evaluating Provenance-Based Intrusion Detection Systems

Recent provenance-based intrusion detection systems PIDSs have demonstrated strong potential for detecting advanced persistent threats APTs by applying machine learning to system provenance graphs. However, evaluating and comparing PIDSs remains difficult: prior work uses inconsistent preprocessi...

RedSage: A Cybersecurity Generalist LLM

Cybersecurity operations demand assistant LLMs that support diverse workflows without exposing sensitive data. Existing solutions either rely on proprietary APIs with privacy risks or on open models lacking domain adaptation. To bridge this gap, we curate 11.8B tokens of cybersecurity-focused...

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...

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...

CVE-2026-22262

CVE-2026-22262 affects Suricata (network IDS/IPS/NSM). When saving a dataset, a stack buffer can overflow if the dataset data is too large, before patches were applied. Affected versions are prior to 8.0.3 and 7.0.14, which include the fixes. Remediation: upgrade to 8.0.3+ or 7.0.14+. As a workar...

CVE-2026-22262 Suricata datasets: stack overflow when saving a set

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...

EUVD-2026-4772

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...

AI-Driven Intrusion Detection for UAV in Smart Urban Ecosystems: A Comprehensive Survey

UAVs have the potential to revolutionize urban management and provide valuable services to citizens. They can be deployed across diverse applications, including traffic monitoring, disaster response, environmental monitoring, and numerous other domains. However, this integration introduces novel...

CAFE-GB: Scalable and Stable Feature Selection for Malware Detection Via Chunk-Wise Aggregated Gradient Boosting

High-dimensional malware datasets often exhibit feature redundancy, instability, and scalability limitations, which hinder the effectiveness and interpretability of machine learning-based malware detection systems. Although feature selection is commonly employed to mitigate these issues, many...

HogVul: Black-Box Adversarial Code Generation Framework against LM-Based Vulnerability Detectors

Recent advances in software vulnerability detection have been driven by Language Model LM-based approaches. However, these models remain vulnerable to adversarial attacks that exploit lexical and syntax perturbations, allowing critical flaws to evade detection. Existing black-box attacks on...

PT-2026-4994

Name of the Vulnerable Software and Affected Versions Suricata versions prior to 8.0.3 Suricata versions prior to 7.0.14 Description Suricata is a network IDS, IPS and NSM engine. A stack buffer overflow can occur while saving a dataset due to the use of a stack buffer to prepare the data. If the...

Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection Via Latent Space Representation Learning and Alignment

Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift...