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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
Real-World Snapshots vs. Theory: Questioning the t-Probin...
Thilo Krachenfels, Fatemeh Ganji, Amir Moradi, Shahin Tajik, Jea · 2020-09-09 · via cs.CR updates on arXiv.org

Due to its sound theoretical basis and practical efficiency, masking has become the most prominent countermeasure to protect cryptographic implementations against physical side-channel attacks (SCAs). The core idea of masking is to randomly split every sensitive intermediate variable during computation into at least t+1 shares, where t denotes the maximum number of shares that are allowed to be observed by an adversary without learning any sensitive information. In other words, it is assumed that the adversary is bounded either by the possessed number of probes (e.g., microprobe needles) or by the order of statistical analyses while conducting higher-order SCA attacks (e.g., differential power analysis). Such bounded models are employed to prove the SCA security of the corresponding implementations. Consequently, it is believed that given a sufficiently large number of shares, the vast majority of known SCA attacks are mitigated. In this work, we present a novel laser-assisted SCA technique, called Laser Logic State Imaging (LLSI), which offers an unlimited number of contactless probes, and therefore, violates the probing security model assumption. This technique enables us to take snapshots of hardware implementations, i.e., extract the logical state of all registers at any arbitrary clock cycle with a single measurement. To validate this, we mount our attack on masked AES hardware implementations and practically demonstrate the extraction of the full-length key in two different scenarios. First, we assume that the location of the registers (key and/or state) is known, and hence, their content can be directly read by a single snapshot. Second, we consider an implementation with unknown register locations, where we make use of multiple snapshots and a SAT solver to reveal the secrets.