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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Protocol-independent Detection of "Messaging Ordering" Ne...
Steffen Wendzel · 2021-02-28 · via cs.CR updates on arXiv.org

Detection methods are available for several known covert channels. However, a type of covert channel that received little attention within the last decade is the "message ordering" channel. Such a covert channel changes the order of PDUs (protocol data units, i.e. packets) transferred over the network to encode hidden information. The advantage of these channels is that they cannot be blocked easily as they do not modify header content but instead mimic typical network behavior such as TCP segments that arrive in a different order than they were sent. Contribution: In this paper, we show a protocol-independent approach to detect message ordering channels. Our approach is based on a modified compressibility score. We analyze the detectability of message ordering channels and whether several types of message ordering channels differ in their detectability. Results: Our results show that the detection of message ordering channels depends on their number of utilized PDUs. First, we performed a rough threshold selection by hand, which we later optimized using the C4.5 decision tree classifier. We were able to detect message ordering covert channels with an accuracy and F1 score of >= 99.5% and a false-positive rate < 1% and < 0.1% if they use sequences of 3 or 4 PDUs, respectively. Simpler channels that only manipulate a sequence of two PDUs were detectable with an accuracy and F1 score of 94.5% and were linked to a false-positive rate of 5.19%. We thus consider our approach suitable for real-world detection scenarios with channels utilizing 3 or 4 PDUs while the detection of channels utilizing 2 PDUs should be improved further.