Machine Learning-Based Detection of MCP Attacks

The Model Context Protocol MCP is a new and emerging technology that extends the functionality of large language models, improving workflows but also exposing users to a new attack surface. Several studies have highlighted related security flaws, but MCP attack detection remains underexplored. To...

CVE-2026-5194

A flaw was found in wolfSSL. Missing hash/digest size and Object Identifier OID checks allow the acceptance of smaller, less secure digests during the verification of Elliptic Curve Digital Signature Algorithm ECDSA certificates. This could enable a remote attacker, with knowledge of the public...

CVE-2026-5194

Missing hash/digest size and OID checks allow digests smaller than allowed when verifying ECDSA certificates, or smaller than is appropriate for the relevant key type, to be accepted by signature verification functions. This could lead to reduced security of ECDSA certificate-based authentication...

The agentic SOC—Rethinking SecOps for the next decade

Every major shift in cyberattacker behavior over the past decade has followed a meaningful shift in how defenders operate. When security operation centers SOCs deployed endpoint detection and response EDR—and later extended detection and response XDR—security teams raised the bar, pushing...

zantetsu-trainer is unmaintained

The zantetsu-trainer crate is no longer maintained. The ML training infrastructure it contained was removed as part of the zantetsu 0.2 release, which replaced the neural parser with a pure heuristic engine. A tombstone version 0.2.0 has been published and 0.1.4 has been yanked. There is no...

Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf COTS components and open-source software has introduced significant cybersecurity vulnerabilities. Ensuring the cybersecuri...

chromium -- security fixes

Chrome Releases reports: This update includes multiple security fixes: Critical: CVE-2026-5858: Heap buffer overflow in WebML. CVE-2026-5859: Integer overflow in WebML. High: CVE-2026-5860: Use after free in WebRTC. CVE-2026-5861: Use after free in V8. CVE-2026-5862: Inappropriate implementation ...

Explainable PQC: A Layered Interpretive Framework for Post-Quantum Cryptographic Security Assumptions

This paper studies how post-quantum cryptographic PQC security assumptions can be represented and communicated through a structured, layered framework that is useful for technical interpretation but does not replace formal cryptographic proofs. We propose "Explainable PQC,'' an interdisciplinary...

Improving ML Attacks on LWE with Data Repetition and Stepwise Regression

The Learning with Errors LWE problem is a hard math problem in lattice-based cryptography. In the simplest case of binary secrets, it is the subset sum problem, with error. Effective ML attacks on LWE were demonstrated in the case of binary, ternary, and small secrets, succeeding on fairly sparse...

ML Defender (ARGus NDR): An Open-Source Embedded ML NIDS for Botnet and Anomalous Traffic Detection in Resource-Constrained Organizations

Ransomware and DDoS attacks disproportionately impact hospitals, schools, and small organizations that cannot afford enterprise security solutions. We present ML Defender aRGus NDR, an open-source network intrusion detection system built in C++20, deployable on commodity hardware at approximately...

CVE-2026-34445

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. Prior to version 1.21.0, the ExternalDataInfo class in ONNX was using Python’s setattr function to load metadata like file paths or data lengths directly from an ONNX model file. It didn’t check if the...

Quantum Bit Error Rate Analysis in BB84 Quantum Key Distribution: Measurement, Statistical Estimation, and Eavesdropping Detection

Quantum Key Distribution QKD provides information-theoretic security by exploiting the principles of quantum mechanics. Among QKD protocols, the BB84 scheme remains the most widely adopted for both theoretical research and practical implementation. A critical parameter determining the reliability...

Context-Aware Phishing Email Detection Using Machine Learning and NLP

Phishing attacks remain among the most prevalent cybersecurity threats, causing significant financial losses for individuals and organizations worldwide. This paper presents a machine learning-based phishing email detection system that analyzes email body content using natural language processing...

CVE-2026-3962

A vulnerability was identified in Jcharis Machine-Learning-Web-Apps up to a6996b634d98ccec4701ac8934016e8175b60eb5. The impacted element is the function rendertemplate of the file Machine-Learning-Web-Apps-master/Build-n-Deploy-Flask-App-with-Waypoint/app/app.py of the component Jinja2 Template...

Machine Learning Operations: Yesterday, Today, and Tomorrow

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Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

The Industrial Internet of Things IIoT introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardwar...

Walma: Learning to See Memory Corruption in WebAssembly

WebAssembly's Wasm monolithic linear memory model facilitates memory corruption attacks that can escalate to cross-site scripting in browsers or go undetected when a malicious host tampers with a module's state. Existing defenses rely on invasive binary instrumentation or custom runtimes, and do...

UBUNTU-CVE-2026-3503

Protection mechanism failure in wolfCrypt post-quantum implementations ML-KEM and ML-DSA in wolfSSL on ARM Cortex-M microcontrollers allows a physical attacker to compromise key material and/or cryptographic outcomes via induced transient faults that corrupt or redirect seed/pointer values during...

CVE-2026-3503

CVE-2026-3503 involves a protection mechanism failure in wolfSSL’s wolfCrypt post-quantum implementations (ML-KEM and ML-DSA) on ARM Cortex-M devices. The root cause is exposed as transient fault injections that can corrupt or redirect seed/pointer values during Keccak-based expansion, potentiall...

CVE-2026-3503 Fault injection attack with ML-DSA and ML-KEM on ARM

Protection mechanism failure in wolfCrypt post-quantum implementations ML-KEM and ML-DSA in wolfSSL on ARM Cortex-M microcontrollers allows a physical attacker to compromise key material and/or cryptographic outcomes via induced transient faults that corrupt or redirect seed/pointer values during...