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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
Restricting Control Flow During Speculative Execution wit...
Zhuojia Shen, Jie Zhou, Divya Ojha, John Criswell · 2019-03-26 · via cs.CR updates on arXiv.org

Side-channel attacks such as Spectre that utilize speculative execution to steal application secrets pose a significant threat to modern computing systems. While program transformations can mitigate some Spectre attacks, more advanced attacks can divert control flow speculatively to bypass these protective instructions, rendering existing defenses useless. In this paper, we present Venkman: a system that employs program transformation to completely thwart Spectre attacks that poison entries in the Branch Target Buffer (BTB) and the Return Stack Buffer (RSB). Venkman transforms code so that all valid targets of a control-flow transfer have an identical alignment in the virtual address space; it further transforms all branches to ensure that all entries added to the BTB and RSB are properly aligned. By transforming all code this way, Venkman ensures that, in any program wanting Spectre defenses, all control-flow transfers, including speculative ones, do not skip over protective instructions Venkman adds to the code segment to mitigate Spectre attacks. Unlike existing defenses, Venkman does not reduce sharing of the BTB and RSB and does not flush these structures, allowing safe sharing and reuse among programs while maintaining strong protection against Spectre attacks. We built a prototype of Venkman on an IBM POWER8 machine. Our evaluation on the SPEC benchmarks and selected applications shows that Venkman increases execution time to 3.47$\times$ on average and increases code size to 1.94$\times$ on average when it is used to ensure that fences are executed to mitigate Spectre attacks. Our evaluation also shows that Spectre-resistant Software Fault Isolation (SFI) built using Venkman incurs a geometric mean of 2.42$\times$ space overhead and 1.68$\times$ performance overhead.