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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
WebPol: Fine-grained Information Flow Policies for Web Br...
Abhishek Bichhawat, Vineet Rajani, Jinank Jain, Deepak Garg, Chr · 2017-06-21 · via cs.CR updates on arXiv.org

In the standard web browser programming model, third-party scripts included in an application execute with the same privilege as the application's own code. This leaves the application's confidential data vulnerable to theft and leakage by malicious code and inadvertent bugs in the third-party scripts. Security mechanisms in modern browsers (the same-origin policy, cross-origin resource sharing and content security policies) are too coarse to suit this programming model. All these mechanisms (and their extensions) describe whether or not a script can access certain data, whereas the meaningful requirement is to allow untrusted scripts access to confidential data that they need and to prevent the scripts from leaking data on the side. Motivated by this gap, we propose WebPol, a policy mechanism that allows a website developer to include fine-grained policies on confidential application data in the familiar syntax of the JavaScript programming language. The policies can be associated with any webpage element, and specify what aspects of the element can be accessed by which third-party domains. A script can access data that the policy allows it to, but it cannot pass the data (or data derived from it) to other scripts or remote hosts in contravention of the policy. To specify the policies, we expose a small set of new native APIs in JavaScript. Our policies can be enforced using any of the numerous existing proposals for information flow tracking in web browsers. We have integrated our policies into one such proposal that we use to evaluate performance overheads and to test our examples.