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Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
An Attack-Driven Incident Response and Defense System (AD...
Anthony Cheuk Tung Lai, Siu Ming Yiu, Ping Fan Ke, Alan Ho · 2025-02-04 · via cs.CR updates on arXiv.org

One of the major goals of incident response is to help an organization or a system owner to quickly identify and halt the attacks to minimize the damages (and financial loss) to the system being attacked. Typical incident responses rely very much on the log information captured by the system during the attacks and if needed, may need to isolate the victim from the network to avoid further destructive attacks. However, there are real cases that there are insufficient log records/information for the incident response team to identify the attacks and their origins while the attacked system cannot be stopped due to service requirements (zero downtime online systems) such as online gaming sites. Typical incident response procedures and industrial standards do not provide an adequate solution to address this scenario. In this paper, being motivated by a real case, we propose a solution, called "Attack-Driven Incident Response and Defense System (ADIRDS)" to tackle this problem. ADIRDS is an online monitoring system to run with the real system. By modeling the real system as a graph, critical nodes/assets of the system are closely monitored. Instead of relying on the original logging system, evidence will be collected from the attack technique perspectives. To migrate the risks, realistic honeypots with very similar business context as the real system are deployed to trap the attackers. We successfully apply this system to a real case. Based on our experiments, we verify that our new approach of designing the realistic honeypots is effective, 38 unique attacker's IP addresses were captured. We also compare the performance of our realistic honey with both low and high interactive honeypots proposed in the literature, the results found that our proposed honeypot can successfully cheat the attackers to attack our honeypot, which verifies that our honeypot is more effective.