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

Cloudbric
Cloudbric
有赞技术团队
有赞技术团队
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
T
Threat Research - Cisco Blogs
L
LangChain Blog
Simon Willison's Weblog
Simon Willison's Weblog
Project Zero
Project Zero
Latest news
Latest news
S
Schneier on Security
Cisco Talos Blog
Cisco Talos Blog
MyScale Blog
MyScale Blog
C
Check Point Blog
IT之家
IT之家
P
Palo Alto Networks Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
C
CERT Recently Published Vulnerability Notes
Scott Helme
Scott Helme
The Hacker News
The Hacker News
C
CXSECURITY Database RSS Feed - CXSecurity.com
G
Google Developers Blog
T
Tor Project blog
T
Threatpost
D
DataBreaches.Net
博客园 - 【当耐特】
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Troy Hunt's Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Vercel News
Vercel News
云风的 BLOG
云风的 BLOG
NISL@THU
NISL@THU
P
Privacy & Cybersecurity Law Blog
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
C
Cisco Blogs
博客园_首页
S
Securelist
T
The Exploit Database - CXSecurity.com
Last Week in AI
Last Week in AI
量子位
U
Unit 42
Know Your Adversary
Know Your Adversary
Hugging Face - Blog
Hugging Face - Blog
S
Security Affairs
Google Online Security Blog
Google Online Security Blog
Hacker News: Ask HN
Hacker News: Ask HN
Webroot Blog
Webroot Blog
S
SegmentFault 最新的问题
Engineering at Meta
Engineering at Meta
N
News and Events Feed by Topic
P
Proofpoint News Feed
阮一峰的网络日志
阮一峰的网络日志

cs.CR updates on arXiv.org

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?
Dynamic S-BOX using Chaotic Map for VPN Data Security
Kashif Ishaq, Khwaja Ahmad Hassan, Yahya Tauqeer Bhatti · 2023-08-26 · via cs.CR updates on arXiv.org

A dynamic SBox using a chaotic map is a cryptography technique that changes the SBox during encryption based on iterations of a chaotic map, adding an extra layer of confusion and security to symmetric encryption algorithms like AES. The chaotic map introduces unpredictability, non-linearity, and key dependency, enhancing the overall security of the encryption process. The existing work on dynamic SBox using chaotic maps lacks standardized guidelines and extensive security analysis, leaving potential vulnerabilities and performance concerns unaddressed. Key management and the sensitivity of chaotic maps to initial conditions are challenges that need careful consideration. The main objective of using a dynamic SBox with a chaotic map in cryptography systems is to enhance the security and robustness of symmetric encryption algorithms. The method of dynamic SBox using a chaotic map involves initializing the SBox, selecting a chaotic map, iterating the map to generate chaotic values, and updating the SBox based on these values during the encryption process to enhance security and resist cryptanalytic attacks. This article proposes a novel chaotic map that can be utilized to create a fresh, lively SBox. The performance assessment of the suggested S resilience Box against various attacks involves metrics such as nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), linear approximation probability (LP), and differential approximation probability (DP). These metrics help gauge the Box ability to handle and respond to different attack scenarios. Assess the cryptography strength of the proposed S-Box for usage in practical security applications, it is compared to other recently developed SBoxes. The comparative research shows that the suggested SBox has the potential to be an important advancement in the field of data security.