110 matches found
Asking AI for personal advice is a bad idea, Stanford study shows
Stanford computer scientists just proved what therapists already suspected: AI chatbots will agree with almost anything you say to keep you happy. The researchers caught these systems validating dangerous decisions just to maintain user engagement. That's a worrying development, especially given...
Secure Reinforcement Learning: On Model-Free Detection of Man in the Middle Attacks
We consider the problem of learning-based man-in-the-middle MITM attacks in cyber-physical systems CPS, and extend our previously proposed Bellman Deviation Detection BDD framework for model-free reinforcement learning RL. We refine the standard MDP attack model by allowing the reward function to...
DeepXplain: XAI-Guided Autonomous Defense against Multi-Stage APT Campaigns
Advanced Persistent Threats APTs are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning DRL enables autonomous cyber defense, its decisions are often opaque and difficult to trust in operational environments. This paper presents DeepXplain, a...
Cyber Deception for Mission Surveillance Via Hypergame-Theoretic Deep Reinforcement Learning
Unmanned Aerial Vehicles UAVs are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Denial-of-Service DoS attacks to overwhelm the resources of mission drones MDs. How can we defend UAV mission systems...
NASimJax: GPU-Accelerated Policy Learning Framework for Penetration Testing
Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inherently partially observable and features large action spaces. Training reinforcement learning RL policies for this domain faces a fundamental...
PISmith: Reinforcement Learning-Based Red Teaming for Prompt Injection Defenses
Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against adaptive attacks remains insufficiently evaluated, potentially creating a false sense of security. In this work, we...
Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To addres...
SlowBA: An Efficiency Backdoor Attack Towards VLM-Based GUI Agents
Modern vision-language-model VLM based graphical user interface GUI agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security...
TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models
The deployment of Large Reasoning Models LRMs in high-stakes decision-making pipelines has introduced a novel and opaque attack surface: reasoning backdoors. In these attacks, the model's intermediate Chain-of-Thought CoT is manipulated to provide a linguistically plausible but logically fallacio...
Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks
Due to the scalability and portability, low-altitude intelligent networks LAINs are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles UAVs are characterized by the distributed topology and high mobility, thus vulnerable to security...
CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions
The evaluation of Large Language Models LLMs for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass. To bridge this gap, we propose CodeHacker, an automated agen...
From SFT to RL: Demystifying the Post-Training Pipeline for LLM-Based Vulnerability Detection
The integration of LLMs into vulnerability detection VD has shifted the field toward interpretable and context-aware analysis. While post-training methods have shown promise in general coding tasks, their systematic application to VD remains underexplored. In this paper, we present the first...
StealthRL: Reinforcement Learning Paraphrase Attacks for Multi-Detector Evasion of AI-Text Detectors
AI-text detectors face a critical robustness challenge: adversarial paraphrasing attacks that preserve semantics while evading detection. We introduce StealthRL, a reinforcement learning framework that stress-tests detector robustness under realistic adversarial conditions. StealthRL trains a...
The Role of Learning in Attacking Intrusion Detection Systems
Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...
SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
Deep Reinforcement Learning DRL has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, transitioning DRL from laboratory simulations to bespoke cyber environments can introduce numerous issues. This is furthe...
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...
ReasoningBomb: A Stealthy Denial-Of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models
Large reasoning models LRMs extend large language models with explicit multi-step reasoning traces, but this capability introduces a new class of prompt-induced inference-time denial-of-service PI-DoS attacks that exploit the high computational cost of reasoning. We first formalize inference cost...
Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report
We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model derived from Llama-3.1-8B-Base, the model is trained through a two-stage process combining supervised fine-tuning SFT and...
Baiting AI: Deceptive Adversary against AI-Protected Industrial Infrastructures
This paper explores a new cyber-attack vector targeting Industrial Control Systems ICS, particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning DRL approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly...
