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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
Credential Masquerading and OpenSSL Spy: Exploring ROS 2 ...
Vincenzo DiLuoffo, William R. Michalson, Berk Sunar · 2019-04-19 · via cs.CR updates on arXiv.org

The trend toward autonomous robot deployments is on an upward growth curve. These robots are undertaking new tasks and are being integrated into society. Examples of this trend are autonomous vehicles, humanoids, and eldercare. The movement from factory floors to streets and homes has also increased the number of vulnerabilities that adversaries can utilize. To improve security, Robot Operating System (ROS) 2 has standardized on using Data Distributed Services (DDS) as the messaging layer, which supports a security standard for protecting messages between parties with access control enforcement. DDS security is dependent on the OpenSSL and a security configuration file that specifies sensitive data location. DSS Security assumes that the underlining Operating System (OS) is secure and that the dependencies are consistent, but ongoing integrity checks are not performed. This paper looks at two vulnerabilities that we exploit using an OpenSSL spy process and a security property file manipulation. An overview of each exploit is provided with an evaluation of mitigation technologies that may be employed in client computers, servers, and other areas. Since, ROS 2 and DDS run in user space, these processes are prone to vulnerabilities. We provide recommendations about mitigation technology, as currently autonomous platforms are being deployed without safe-guards for on or off-line threats. The Trust Platform Module (TPM) is new to robotic systems, but the standard usage model does not provide risk mitigation above the OS layer for the types of attacks we discuss.