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cs.CR updates on arXiv.org

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
Detecting Domain-Generation Algorithm (DGA) Based Fully-Q...
Adam Dorian Wong · 2023-04-17 · via cs.CR updates on arXiv.org

Domain Name System (DNS) is the backbone of the Internet. However, threat actors have abused the antiquated protocol to facilitate command-and-control (C2) actions, to tunnel, or to exfiltrate sensitive information in novel ways. The FireEye breach and Solarwinds intrusions of late 2020 demonstrated the sophistication of hacker groups. Researchers were eager to reverse-engineer the malware and eager to decode the encrypted traffic. Noticeably, organizations were keen on being first to "solve the puzzle". Dr. Eric Cole of SANS Institute routinely expressed "prevention is ideal, but detection is a must". Detection analytics may not always provide the underlying context in encrypted traffic, but will at least give a fighting chance for defenders to detect the anomaly. SUNBURST is an open-source moniker for the backdoor that affected Solarwinds Orion. While analyzing the malware with security vendor research, there is a possible single-point-of-failure in the C2 phase of the Cyber Kill Chain provides an avenue for defenders to exploit and detect the activity itself. One small chance is better than none. The assumption is that encryption increases entropy in strings. SUNBURST relied on encryption to exfiltrate data through DNS queries of which the adversary prepended to registered Fully-Qualified Domain Names (FQDNs). These FQDNs were typo-squatted to mimic Amazon Web Services (AWS) domains. SUNBURST detection is possible through a simple 1-variable t-test across all DNS logs for a given day. The detection code is located on GitHub (https://github.com/MalwareMorghulis/SUNBURST).