惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Y
Y Combinator Blog
The GitHub Blog
The GitHub Blog
Vercel News
Vercel News
D
DataBreaches.Net
MongoDB | Blog
MongoDB | Blog
H
Help Net Security
小众软件
小众软件
美团技术团队
T
The Blog of Author Tim Ferriss
爱范儿
爱范儿
D
Docker
Martin Fowler
Martin Fowler
大猫的无限游戏
大猫的无限游戏
博客园 - 聂微东
Blog — PlanetScale
Blog — PlanetScale
H
Hackread – Cybersecurity News, Data Breaches, AI and More
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
V2EX
S
SegmentFault 最新的问题
云风的 BLOG
云风的 BLOG
B
Blog
雷峰网
雷峰网
The Cloudflare Blog

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
RTL2M$μ$PATH: Multi-$μ$PATH Synthesis with Applications t...
Yao Hsiao, Nikos Nikoleris, Artem Khyzha, Dominic P. Mulligan, G · 2024-09-29 · via cs.CR updates on arXiv.org

The Check tools automate formal memory consistency model and security verification of processors by analyzing abstract models of microarchitectures, called $μ$SPEC models. Despite the efficacy of this approach, a verification gap between $μ$SPEC models, which must be manually written, and RTL limits the Check tools' broad adoption. Our prior work, called RTL2$μ$SPEC, narrows this gap by automatically synthesizing formally verified $μ$SPEC models from SystemVerilog implementations of simple processors. But, RTL2$μ$SPEC assumes input designs where an instruction (e.g., a load) cannot exhibit more than one microarchitectural execution path ($μ$PATH, e.g., a cache hit or miss path) -- its single-execution-path assumption. In this paper, we first propose an automated approach and tool, called RTL2M$μ$PATH, that resolves RTL2$μ$SPEC's single-execution-path assumption. Given a SystemVerilog processor design, instruction encodings, and modest design metadata, RTL2M$μ$PATH finds a complete set of formally verified $μ$PATHs for each instruction. Next, we make an important observation: an instruction that can exhibit more than one $μ$PATH strongly indicates the presence of a microarchitectural side channel in the input design. Based on this observation, we then propose an automated approach and tool, called SynthLC, that extends RTL2M$μ$PATH with a symbolic information flow analysis to support synthesizing a variety of formally verified leakage contracts from SystemVerilog processor designs. Leakage contracts are foundational to state-of-the-art defenses against hardware side-channel attacks. SynthLC is the first automated methodology for formally verifying hardware adherence to them.