Strategic Heterogeneous Multi-Agent Architecture for Cost-Effective Code Vulnerability Detection

Automated code vulnerability detection is critical for software security, yet existing approaches face a fundamental trade-off between detection accuracy and computational cost. We propose a heterogeneous multi-agent architecture inspired by game-theoretic principles, combining cloud-based LLM...

AVISE: Framework for Evaluating the Security of AI Systems

As artificial intelligence AI systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of high-profile exploits and consequential system failures. Yet systematic approaches to evaluating AI security remain underdeveloped. In this paper, we...

BreachLock Named Representative Vendor in the 2026 Gartner Market Guide for Adversarial Exposure Validation

New York, United States, 21st April 2026, CyberNewswire...

Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection

Cross-site scripting XSS remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious payload while preserving its behavior. These transformations make it difficult for traditional and machine learning-based detection systems to reliab...

ARES: Adaptive Red-Teaming and End-To-End Repair of Policy-Reward System

Reinforcement Learning from Human Feedback RLHF is central to aligning Large Language Models LLMs, yet it introduces a critical vulnerability: an imperfect Reward Model RM can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches...

GuardPhish: Securing Open-Source LLMs from Phishing Abuse

The rapid adoption of open-source Large Language Models LLMs in offline and enterprise environments has introduced a largely unexamined security risk like susceptibility to adversarial phishing prompts under static safety configurations. In this work, we systematically investigate this...

accutuning-helpers (>=1.0.32 <=1.0.33), adaptnlp (>=0.3.0 <=0.3.7) +239 more potentially affected by CVE-2026-40491 via gdown (>=3.11.0 <=5.2.1)

gdown PYPI version =3.11.0, =1.0.32, =0.3.0, =0.0.0, =0.2.0, =0.0.2, =1.14.0, =0.4.0, =0.1.1, =0.0.1, =1.2.14 and more Source cves: CVE-2026-40491 Source advisory: OSV:GHSA-76HW-P97H-883F...

vulnswarm

VulnSwarm AI-powered vulnerability discovery using multi-agen...

Robust Semi-Supervised Temporal Intrusion Detection for Adversarial Cloud Networks

Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However, real-world deployments are challenged by limited labeled data, non-stationary traffic, and adaptive adversaries. While semi-supervised learning can...

ClawLess: A Security Model of AI Agents

Autonomous AI agents powered by Large Language Models can reason, plan, and execute complex tasks, but their ability to autonomously retrieve information and run code introduces significant security risks. Existing approaches attempt to regulate agent behavior through training or prompting, which...

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a rece...

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts

The deployment of large language models LLMs in Swiss financial and regulatory contexts demands empirical evidence of both production reliability and adversarial security, dimensions not jointly operationalized in existing Swiss-focused evaluation frameworks. This paper introduces Swiss-Bench 003...

Explainable Autonomous Cyber Defense Using Adversarial Multi-Agent Reinforcement Learning

Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat APT actors exploit "Living off the Land" techniques and targeted telemetry perturbations ...

SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement

LLM-based agent systems increasingly rely on agent skills sourced from open registries to extend their capabilities, yet the openness of such ecosystems makes skills difficult to thoroughly vet. Existing attacks rely on injecting malicious instructions into skills, making them easily detectable b...

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs

Transformer-based malware detection systems operating on graph modalities such as control flow graphs CFGs achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the...

AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

As TLS 1.3 encryption limits traditional Deep Packet Inspection DPI, the security community has pivoted to Euclidean Transformer-based classifiers e.g., ET-BERT for encrypted traffic analysis. However, these models remain vulnerable to byte-level adversarial morphing -- recent pre-padding attacks...

a-mailx (=0.1.0), a2a-acl (=0.0.15) +1340 more potentially affected by CVE-2026-34515 via aiohttp (>=0.13.1 <=3.13.3)

aiohttp PYPI version =0.13.1, =0.1.1, =0.1.0b0, =1.1.0, =1.0.1, =0.0.0, =0.0.2, =4.8.2, =0.0.3, =0.1.3, =0.4.0, =56.0.0, =72.0.0 and more Source cves: CVE-2026-34515 Source advisory: OSV:GHSA-P998-JP59-783M...

SafeClaw-R: Towards Safe and Secure Multi-Agent Personal Assistants

LLM-based multi-agent systems MASs are transforming personal productivity by autonomously executing complex, cross-platform tasks. Frameworks such as OpenClaw demonstrate the potential of locally deployed agents integrated with personal data and services, but this autonomy introduces significant...

Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models

Large language models LLMs increasingly rely on explicit chain-of-thought CoT reasoning to solve complex tasks, yet the safety of the reasoning process itself remains largely unaddressed. Existing work on LLM safety focuses on content safety--detecting harmful, biased, or factually incorrect...

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...