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

Microsoft Azure Blog
Microsoft Azure Blog
宝玉的分享
宝玉的分享
博客园 - 【当耐特】
有赞技术团队
有赞技术团队
G
Google Developers Blog
Microsoft Security Blog
Microsoft Security Blog
Apple Machine Learning Research
Apple Machine Learning Research
The Cloudflare Blog
Blog — PlanetScale
Blog — PlanetScale
博客园_首页
L
LangChain Blog
Stack Overflow Blog
Stack Overflow Blog
Last Week in AI
Last Week in AI
Y
Y Combinator Blog
罗磊的独立博客
T
Tailwind CSS Blog
博客园 - 叶小钗
T
The Blog of Author Tim Ferriss
Engineering at Meta
Engineering at Meta
博客园 - 聂微东
博客园 - Franky
B
Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
F
Fortinet All Blogs

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
Efficient Verification of Optimized Code: Correct High-sp...
Marc Schoolderman, Jonathan Moerman, Sjaak Smetsers, Marko van E · 2020-12-18 · via cs.CR updates on arXiv.org

Code that is highly optimized poses a problem for program-level verification: programmers can employ various clever tricks that are non-trivial to reason about. For cryptography on low-power devices, it is nonetheless crucial that implementations be functionally correct, secure, and efficient. These are usually crafted in hand-optimized machine code that eschew conventional control flow as much as possible. We have formally verified such code: a library which implements elliptic curve cryptography on 8-bit AVR microcontrollers. The chosen implementation is the most efficient currently known for this microarchitecture. It consists of over 3000 lines of assembly instructions. Building on earlier work, we use the Why3 platform to model the code and prove verification conditions, using automated provers. We expect the approach to be re-usable and adaptable, and it allows for validation. Furthermore, an error in the original implementation was found and corrected, at the same time reducing its memory footprint. This shows that practical verification of cutting-edge code is not only possible, but can in fact add to its efficiency -- and is clearly necessary.