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

让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
T
The Blog of Author Tim Ferriss
博客园 - 司徒正美
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
有赞技术团队
有赞技术团队
量子位
S
SegmentFault 最新的问题
博客园 - 聂微东
博客园 - 【当耐特】
J
Java Code Geeks
美团技术团队
Hugging Face - Blog
Hugging Face - Blog
H
Help Net Security
V
V2EX
人人都是产品经理
人人都是产品经理
博客园 - Franky
罗磊的独立博客
Engineering at Meta
Engineering at Meta
A
About on SuperTechFans
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
酷 壳 – CoolShell
酷 壳 – CoolShell
云风的 BLOG
云风的 BLOG
Y
Y Combinator Blog
Apple Machine Learning Research
Apple Machine Learning Research

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
On Designing A Questionnaire Based Legacy-UI Honeyword Ge...
Nilesh Chakraborty, Shreya Singh, Samrat Mondal · 2017-08-04 · via cs.CR updates on arXiv.org

Modern trend sees a lot usage of \textit{honeywords} (or fake password) for protecting the original passwords in the password file. However, the usage of \textit{honeywords} has strongly been criticized under the different security and usability parameters. Though many of these issues have been successfully resolved, research in this domain is still facing difficulties in \textit{achieving flatness} (or producing the equally probable \textit{honeywords} with reference to the original password). Though recent studies have made a significant effort to meet this criterion, we show that they either fall short or are based on some unrealistic assumptions. To practically fulfill this flatness criterion, we propose a questionnaire-oriented authentication system based on the episodic (or long term) memory of the users. Our study reveals that proposed mechanism is capable of generating significantly improved flatter list of \textit{honeywords} compared to the existing protocols. The subsequent discussion shows that the proposed system also overcomes all the limitations of the existing state of arts with no lesser than $95\%$ goodness.