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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
A Deep Dive Into How Open-Source Project Maintainers Revi...
Jessy Ayala, Steven Ngo, Joshua Garcia · 2024-09-12 · via cs.CR updates on arXiv.org

Researchers have investigated the bug bounty ecosystem from the lens of platforms, programs, and bug hunters. Understanding the perspectives of bug bounty report reviewers, especially those who historically lack a security background and little to no funding for bug hunters, is currently understudied. In this paper, we primarily investigate the perspective of open-source software (OSS) maintainers who have used \texttt{huntr}, a bug bounty platform that pays bounties to bug hunters who find security bugs in GitHub projects and have had valid vulnerabilities patched as a result. We address this area by conducting three studies: identifying characteristics through a listing survey ($n_1=51$), their ranked importance with Likert-scale survey data ($n_2=90$), and conducting semi-structured interviews to dive deeper into real-world experiences ($n_3=17$). As a result, we categorize 40 identified characteristics into benefits, challenges, helpful features, and wanted features. We find that private disclosure and project visibility are the most important benefits, while hunters focused on money or CVEs and pressure to review are the most challenging to overcome. Surprisingly, lack of communication with bug hunters is the least challenging, and CVE creation support is the second-least helpful feature for OSS maintainers when reviewing bug bounty reports. We present recommendations to make the bug bounty review process more accommodating to open-source maintainers and identify areas for future work.