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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
FIPAC: Thwarting Fault- and Software-Induced Control-Flow...
Robert Schilling, Pascal Nasahl, Stefan Mangard · 2021-04-30 · via cs.CR updates on arXiv.org

With the improvements of computing technology, more and more applications embed powerful ARM processors into their devices. These systems can be attacked by redirecting the control-flow of a program to bypass critical pieces of code such as privilege checks or signature verifications. Control-flow hijacks can be performed using classical software vulnerabilities, physical fault attacks, or software-induced fault attacks. To cope with this threat and to protect the control-flow, dedicated countermeasures are needed. To counteract control-flow hijacks, control-flow integrity~(CFI) aims to be a generic solution. However, software-based CFI typically either protects against software or fault attacks, but not against both. While hardware-assisted CFI can mitigate both types of attacks, they require extensive hardware modifications. As hardware changes are unrealistic for existing ARM architectures, a wide range of systems remains unprotected and vulnerable to control-flow attacks. In this work, we present FIPAC, an efficient software-based CFI scheme protecting the execution at basic block granularity of ARM-based devices against software and fault attacks. FIPAC exploits ARM pointer authentication of ARMv8.6-A to implement a cryptographically signed control-flow graph. We cryptographically link the correct sequence of executed basic blocks to enforce CFI at this granularity. We use an LLVM-based toolchain to automatically instrument programs. The evaluation on SPEC2017 with different security policies shows a code overhead between 54-97\% and a runtime overhead between 35-105%. While these overheads are higher than for countermeasures against software attacks, FIPAC outperforms related work protecting the control-flow against fault attacks. FIPAC is an efficient solution to provide protection against software- and fault-based CFI attacks on basic block level on modern ARM devices.