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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
ARMORY: Fully Automated and Exhaustive Fault Simulation o...
Max Hoffmann, Falk Schellenberg, Christof Paar · 2021-05-28 · via cs.CR updates on arXiv.org

Embedded systems are ubiquitous. However, physical access of users and likewise attackers makes them often threatened by fault attacks: a single fault during the computation of a cryptographic primitive can lead to a total loss of system security. This can have serious consequences, e.g., in safetycritical systems, including bodily harm and catastrophic technical failures. However, countermeasures often focus on isolated fault models and high layers of abstraction. This leads to a dangerous sense of security, because exploitable faults that are only visible at machine code level might not be covered by countermeasures. In this work we present ARMORY, a fully automated open source framework for exhaustive fault simulation on binaries of the ubiquitous ARM-M class. It allows engineers and analysts to efficiently scan a binary for potential weaknesses against arbitrary combinations of multi-variate fault injections under a large variety of fault models. Using ARMORY, we demonstrate the power of fully automated fault analysis and the dangerous implications of applying countermeasures without knowledge of physical addresses and offsets. We exemplarily analyze two case studies, which are highly relevant for practice: a DFA on AES (cryptographic) and a secure bootloader (non-cryptographic). Our results show that indeed numerous exploitable faults found by ARMORY which occur in the actual implementations are easily missed in manual inspection. Crucially, most faults are only visible when taking machine code information, i.e., addresses and offsets, into account. Surprisingly, we show that a countermeasure that protects against one type of fault can actually largely increase the vulnerability to other fault models. Our work demonstrates the need for countermeasures that, at least in their evaluation, are not restricted to isolated fault models and consider low-level information [...].