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Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks CTFusion: A CTF-based Benchmark for LLM Agent Evaluation Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs Containment Verification: AI Safety Guarantees Independent of Alignment Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents From Specification to Deployment: Empirical Evidence from a W3C VC + DID Trust Infrastructure for Autonomous Agents 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 Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models 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 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 StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks Feedback Lunch: Learned Feedback Codes for Secure Communications Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models A First Look at the Security Issues in the Model Context Protocol Ecosystem Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems MEASER: Malware embedding attacks on open-source LLMs Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG) From surveillance to signalling: escalation channels as environmental controls for agentic AI Quantitative Certification of Agentic Tool Selection STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids Guidance Watermarking for Diffusion Models SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs A Comprehensive Guide to Differential Privacy: From Theory to User Expectations Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution Searching for Privacy Risks in LLM Agents via Simulation Exact Verification of Graph Neural Networks with Incremental Constraint Solving SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction Majority Bit-Aware Watermarking For Large Language Models Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection Prompt to Pwn: Automated Exploit Generation for Smart Contracts Activation-Guided Local Editing for Jailbreaking Attacks Random Walk Learning and the Pac-Man Attack ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation White-Basilisk: A Hybrid Model for Code Vulnerability Detection Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem Exploring the Secondary Risks of Large Language Models Benchmarking Misuse Mitigation Against Covert Adversaries Efficient Preimage Approximation for Neural Network Certification Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection PARASITE: Conditional System Prompt Poisoning to Hijack LLMs Secure LLM Fine-Tuning via Safety-Aware Probing Can Large Language Models Really Recognize Your Name? PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron? AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion Chronology of Multi-Agent Interactions for Provenance of Evolving Information Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models DeePen: Penetration Testing for Audio Deepfake Detection Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC) How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies Imitation Game for Adversarial Disillusion with Chain-of-Thought Reasoning in Generative AI PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis Red-Teaming Text-to-Image Models via In-Context Experience Replay and Semantic-Preserving Prompt Rewriting DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning Privacy Leakage via Output Label Space and Differentially Private Continual Learning ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment Power-Softmax: Towards Secure LLM Inference over Encrypted Data Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery Anomaly Detection from a Tensor Train Perspective Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness Convergent Differential Privacy Analysis for General Federated Learning Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence Towards Agentic Runtime Healing Verification of Machine Unlearning is Fragile Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning Whispers in the Machine: Confidentiality in Agentic Systems MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Towards Adaptive, Learning-Based Security in Decentralized Applications Can Blockchains Reliably Train Machine Learning Models?
FIPAC: Thwarting Fault- and Software-Induced Control-Flow Attacks with ARM Pointer Authentication
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.