TrojanWorld: Backdooring World-Model Agents Via Imagination Steering

World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reason over imagined trajectories before acting. Their substantial training demands make pretrained world models attractive for distribution and reuse...

REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model realistic adversaries, as they often assume access to privileged information...

ContextLeak: Exfiltrating LLM Agent Context Via Malicious Tools

Exfiltrating an LLM agent's runtime context -- such as the user prompt, execution trajectory, and tool list -- poses severe security and privacy risks to users. Such attacks can be carried out via malicious tools and typically require three conditions: 1 the agent selects the malicious tool for...

Answer Is Cheap, Show Me the Evidence! Augmenting Automated Vulnerability Assessment with Evidence

Software vulnerability SV assessment helps prioritize remediation by characterizing reported vulnerabilities. Existing automated methods predict assessment results from SV reports SVRs, but often overlook information in rich text, such as screenshots and code snippets, as well as contextual...

OpenAI Pauses Frontier RL Training as It Tightens Defenses Against Unsafe AI Behavior

OpenAI on Tuesday revealed that it paused reinforcement learning RL training for its latest artificial intelligence AI models for two weeks while it shored up additional defenses and increased the scope of its monitoring to avert another Hugging Face-like incident. "As models become more capable,...

Proving the Utility of Large Language Models in Cybersecurity Simulations: A Comprehensive Examination

Cyber threats continue to escalate in both frequency and sophistication, necessitating more adaptive and scalable defense strategies. This paper explores how Large Language Models LLMs can bolster cybersecurity simulations by automating the creation of synthetic environments and identifying laten...

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework. We deploy a Deep Q-Network DQN to...

Dueling Deep Q-Learning for Intrusion Detection

Intrusion detection systems IDS and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a...

Antares: Foundation Models for Agentic Vulnerability Localization

Vulnerability localization is a fundamental step in software security, requiring models to reason over large codebases and iteratively identify vulnerable implementations. We present Antares, a family of compact language models 350M, 1B, and 3B parameters for agentic vulnerability localization...

Cybersecurity Detection Classification with Reasoning-Enabled Language Models

A major issue in Security Operations Centers SOCs is alert fatigue, as the number of detections reported is more than staff can triage in a given day. Prior work prompts or fine-tunes large language models LLMs to emit a triage label directly, but does not train them to reason about whether a...

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from outside the function to be classified correctly. Agentic reinforcement learni...

VulnGym: Evaluating Vulnerability Management Strategies against Advanced Persistent Threats

Enterprise networks are continuously targeted by Advanced Persistent Threats APTs, attack campaigns exploiting software vulnerabilities to compromise critical assets over time. As disclosed vulnerabilities grow, resource-constrained organizations must prioritize which ones to patch. Existing...

abva

Autonomous Binary Vulnerability Agent ABVA Educational po...

From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python

The original ALPHA benchmark introduced a taxonomy-aware penalty for evaluating CWE-level vulnerability prediction in Python and proposed that the penalty could theoretically also serve as a training signal. This paper provides that validation. We compare three delivery mechanisms: supervised...

AI in Cyberpsychology: A Systematic Literature Review of Cybersecurity Enhancement by Using AI for Analyzing Psychology of Victims, Attackers, and Defenders

Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology CPSY is defined as the use of psychology to enhance cybersecurity applications. Since the early 2010s, the evolution of Artificial Intelligence AI has increasingly integrated with CPSY,...

Securing Autonomous Vehicle Systems Via Twin-Aware Federated Reinforcement Learning

Federated reinforcement learning FRL is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments. However, poisoning attacks pose a significant threat...

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design

Ransomware detection is a security-critical task in which false negatives and false positives have unequal operational consequences. Conventional machine learning detectors often use symmetric objectives that penalize missed ransomware detections and benign false alarms equally, although a false...

An AI-Based Solution for Secure Service Provisioning in IoT

As the Internet of Things IoT continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this evolving landscape, securing service provisioning is crucial to ensure the proper functioning, security, and reliability...

Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis

Vulnerability detection in C/C++ software remains a major security challenge due to code complexity, manual memory management, and the limitations of traditional static analysis. Reinforcement Learning RL has emerged as a promising approach, particularly for fuzzing, test generation, program...

Binary Decompilation LLM with Feedback-Driven Multi-Turn Refinement

Binary decompilation is fundamental to security tasks such as vulnerability discovery, malware inspection, and executable-only program understanding. Recent LLM-based decompilation methods have shown promising results, but most still follow a single-turn generation paradigm: given assembly code o...