From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation

Machine-learning-based Intrusion Detection Systems IDS have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes...

azure-ai-generative (>=1.0.0b1 <=1.0.0b3), azure-ai-resources (>=1.0.0b1 <=1.0.0b9) +15 more potentially affected by CVE-2026-2652 via mlflow-skinny (>=3.0.0 <=3.0.1)

mlflow-skinny PYPI version =3.0.0, =1.0.0b1, =1.0.0b1, =0.1.0, =0.1.0, =2.5.0, =0.0.13, =3.0.0, =0.1.0, =0.1.4 and more Source cves: CVE-2026-2652 Source advisory: SNYK:PYTHON-MLFLOWSKINNY-16698136...

Convolutional-Neural-Networks for Deanonymisation of I2P Traffic

This study investigates the potential for deanonymizing services within the Invisible Internet Project I2P network through passive traffic analysis and machine learning techniques. The primary objective is to identify distinctive patterns in I2P traffic despite the encryption of its payload. To...

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...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

EUVD-2025-209448

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

CVE-2025-69893 describes a side-channel vulnerability in BIP-39 mnemonic processing observed in Trezor hardware wallets (One v1.13.0–v1.14.0, T v1.13.0–v1.14.0, Safe v1.13.0–v1.14.0). The root cause is non-constant time execution and specific branch patterns during word search dictated by the BIP...

PT-2026-32627

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection

Stepping-stone intrusions SSIs are a prevalent network evasion technique in which attackers route sessions through chains of compromised intermediate hosts to obscure their origin. Effective SSI detection requires correlating the incoming and outgoing flows at each relay host at extremely low fal...

azure-ai-generative (>=1.0.0b1 <=1.0.0b3), azure-ai-resources (>=1.0.0b1 <=1.0.0b9) +24 more potentially affected by CVE-2025-15381 via mlflow-skinny (>=3.0.0 <=3.11.0rc0)

mlflow-skinny PYPI version =3.0.0, =1.0.0b1, =1.0.0b1, =0.1.0, =0.1.0, =2.5.0, =0.0.13, =7.1.1, =3.0.0, =3.11.0rc0 and more Source cves: CVE-2025-15381 Source advisory: SNYK:PYTHON-MLFLOWSKINNY-15870197...

Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC under Channel Uncertainty

Integrated sensing and communication ISAC systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel...

Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face

Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critical vulnerability in passport enrollment procedures adopted by many countries, where the facial image is often acquire...

An Empirical Study of the Imbalance Issue in Software Vulnerability Detection

Vulnerability detection is crucial to protect software security. Nowadays, deep learning DL is the most promising technique to automate this detection task, leveraging its superior ability to extract patterns and representations within extensive code volumes. Despite its promise, DL-based...

GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks

GPU memory errors are a critical threat to deep learning DL frameworks, leading to crashes or even security issues. We introduce GPU-Fuzz, a fuzzer locating these issues efficiently by modeling operator parameters as formal constraints. GPU-Fuzz utilizes a constraint solver to generate test cases...

Helper-Assisted Coding for Gaussian Wiretap Channels: Deep Learning Meets PhySec

Consider the Gaussian wiretap channel, where a transmitter wishes to send a confidential message to a legitimate receiver in the presence of an eavesdropper. It is well known that if the eavesdropper experiences less channel noise than the legitimate receiver, then it is impossible for the...

NVIDIA RunX security vulnerabilities

NVIDIA runx is a deep learning experiment management tool developed by NVIDIA Corporation. NVIDIA runx contains a security vulnerability, which stems from code injection. This vulnerability may lead to code execution, denial of service, privilege escalation, information leakage, and data corrupti...

MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-Of-Distribution Malware Detection and Classification

Out of distribution OOD detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to...