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

aimingoo的专栏
aimingoo的专栏
宝玉的分享
宝玉的分享
J
Java Code Geeks
Martin Fowler
Martin Fowler
博客园 - Franky
I
InfoQ
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
S
SegmentFault 最新的问题
B
Blog
The Cloudflare Blog
F
Fortinet All Blogs
量子位
腾讯CDC
博客园 - 司徒正美
D
Docker
大猫的无限游戏
大猫的无限游戏
Microsoft Azure Blog
Microsoft Azure Blog
T
The Blog of Author Tim Ferriss
V
Visual Studio Blog
IT之家
IT之家
Last Week in AI
Last Week in AI
D
DataBreaches.Net
小众软件
小众软件

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
RandOhm: Mitigating Impedance Side-channel Attacks using ...
Saleh Khalaj Monfared, Domenic Forte, Shahin Tajik · 2024-01-17 · via cs.CR updates on arXiv.org

Physical side-channel attacks can compromise the security of integrated circuits. Most physical side-channel attacks (e.g., power or electromagnetic) exploit the dynamic behavior of a chip, typically manifesting as changes in current consumption or voltage fluctuations where algorithmic countermeasures, such as masking, can effectively mitigate them. However, as demonstrated recently, these mitigation techniques are not entirely effective against backscattered side-channel attacks such as impedance analysis. In the case of an impedance attack, an adversary exploits the data-dependent impedance variations of the chip power delivery network (PDN) to extract secret information. In this work, we introduce RandOhm, which exploits a moving target defense (MTD) strategy based on the partial reconfiguration (PR) feature of mainstream FPGAs and programmable SoCs to defend against impedance side-channel attacks. We demonstrate that the information leakage through the PDN impedance could be significantly reduced via runtime reconfiguration of the secret-sensitive parts of the circuitry. Hence, by constantly randomizing the placement and routing of the circuit, one can decorrelate the data-dependent computation from the impedance value. Moreover, in contrast to existing PR-based countermeasures, RandOhm deploys open-source bitstream manipulation tools on programmable SoCs to speed up the randomization and provide real-time protection. To validate our claims, we apply RandOhm to AES ciphers realized on 28-nm FPGAs. We analyze the resiliency of our approach by performing non-profiled and profiled impedance analysis attacks and investigate the overhead of our mitigation in terms of delay and performance.