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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
Risk Assessment of Cyber Attacks on Telemetry Enabled Car...
Ngamboé Mikaela, Berthier Paul, Ammari Nader, Dyrda Katia, Ferna · 2019-04-26 · via cs.CR updates on arXiv.org

Cardiac Implantable Electronic Devices (CIED) are fast becoming a fundamental tool of advanced medical technology and a key instrument in saving lives. Despite their importance, previous studies have shown that CIED are not completely secure against cyber attacks and especially those who are exploiting their Radio Frequency (RF) communication interfaces. Furthermore, the telemetry capabilities and IP connectivity of the external devices interacting with the CIED are creating other entry points that may be used by attackers. In this paper, we carry out a realistic risk analysis of such attacks. This analysis is composed of three parts. First, an actor-based analysis to determine the impact of the attacks. Second, a scenario-based analysis to determine the probability of occurrence of each threat. Finally, a combined analysis to determine which attack outcomes (i.e. attack goals) are riskiest and to identify the vulnerabilities that constitute the highest overall risk exposure. The conducted study showed that the vulnerabilities associated with the RF interface of CIED represent an acceptable risk. In contrast, the network and internet connectivity of external devices represent an important potential risk. The previously described findings suggest that the highest risk is associated with external systems and not the CIED itself.