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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
Automatic detection of access control vulnerabilities via...
Alexander Barabanov, Denis Dergunov, Denis Makrushin, Aleksey Te · 2022-01-26 · via cs.CR updates on arXiv.org

Objective. Insecure Direct Object Reference (IDOR) or Broken Object Level Authorization (BOLA) are one of the critical type of access control vulnerabilities for modern applications. As a result, an attacker can bypass authorization checks leading to information leakage, account takeover. Our main research goal was to help an application security architect to optimize security design and testing process by giving an algorithm and tool that allows to automatically analyze system API specifications and generate list of possible vulnerabilities and attack vector ready to be used as security non-functional requirements. Method. We conducted a multivocal review of research and conference papers, bug bounty program reports and other grey sources of literature to outline patterns of attacks against IDOR vulnerability. These attacks are collected in groups proceeding with further analysis common attributes between these groups and what features compose the group. Endpoint properties and attack techniques comprise a group of attacks. Mapping between group features and existing OpenAPI specifications is performed to implement a tool for automatic discovery of potentially vulnerable endpoints. Results and practical relevance. In this work, we provide systematization of IDOR/BOLA attack techniques based on literature review, real cases analysis and derive IDOR/BOLA attack groups. We proposed an approach to describe IDOR/BOLA attacks based on OpenAPI specifications properties. We develop an algorithm of potential IDOR/BOLA vulnerabilities detection based on OpenAPI specification processing. We implemented our novel algorithm using Python and evaluated it. The results show that algorithm is resilient and can be used in practice to detect potential IDOR/BOLA vulnerabilities.