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

Jina AI
Jina AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog
T
The Blog of Author Tim Ferriss
量子位
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky
小众软件
小众软件
Recent Announcements
Recent Announcements
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
I
InfoQ
美团技术团队
G
Google Developers Blog
Engineering at Meta
Engineering at Meta
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
云风的 BLOG
云风的 BLOG
博客园 - 【当耐特】
IT之家
IT之家
Microsoft Security Blog
Microsoft Security Blog
博客园 - 聂微东
Last Week in AI
Last Week in AI
H
Hackread – Cybersecurity News, Data Breaches, AI and More
H
Help Net Security

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 The Limitation of Some Fully Observable Multiple Sessi...
Nilesh Chakraborty, Samrat Mondal · 2017-05-31 · via cs.CR updates on arXiv.org

Using password based authentication technique, a system maintains the login credentials (username, password) of the users in a password file. Once the password file is compromised, an adversary obtains both the login credentials. With the advancement of technology, even if a password is maintained in hashed format, then also the adversary can invert the hashed password to get the original one. To mitigate this threat, most of the systems nowadays store some system generated fake passwords (also known as honeywords) along with the original password of a user. This type of setup confuses an adversary while selecting the original password. If the adversary chooses any of these honeywords and submits that as a login credential, then system detects the attack. A large number of significant work have been done on designing methodologies (identified as $\text{M}^{\text{DS}}_{\text{OA}}$) that can protect password against observation or, shoulder surfing attack. Under this attack scenario, an adversary observes (or records) the login information entered by a user and later uses those credentials to impersonate the genuine user. In this paper, we have shown that because of their design principle, a large subset of $\text{M}^{\text{DS}}_{\text{OA}}$ (identified as $\text{M}^{\text{FODS}}_{\text{SOA}}$) cannot afford to store honeywords in password file. Thus these methods, belonging to $\text{M}^{\text{FODS}}_{\text{SOA}}$, are unable to provide any kind of security once password file gets compromised. Through our contribution in this paper, by still using the concept of honeywords, we have proposed few generic principles to mask the original password of $\text{M}^{\text{FODS}}_{\text{SOA}}$ category methods. We also consider few well-established methods like S3PAS, CHC, PAS and COP belonging to $\text{M}^{\text{FODS}}_{\text{SOA}}$, to show that proposed idea is implementable in practice.