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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
PARIOT: Anti-Repackaging for IoT Firmware Integrity
Luca Verderame, Antonio Ruggia, Alessio Merlo · 2021-09-09 · via cs.CR updates on arXiv.org

IoT repackaging refers to an attack devoted to tampering with a legitimate firmware package by modifying its content (e.g., injecting some malicious code) and re-distributing it in the wild. In such a scenario, the firmware delivery and update processes play a central role in ensuring firmware integrity. Unfortunately, several existing solutions lack proper integrity verification, exposing firmware to repackaging attacks. If this is not the case, they still require an external trust anchor (e.g., signing keys or secure storage technologies), which could limit their adoption in resource-constrained environments. In addition, state-of-the-art frameworks do not cope with the entire firmware production and delivery process, thereby failing to protect the content generated by the firmware producers through the whole supply chain. To mitigate such a problem, in this paper, we introduce PARIOT, a novel self-protecting scheme for IoT that allows the injection of integrity checks, called anti-tampering (AT) controls, directly into the firmware. The AT controls enable the runtime detection of repackaging attempts without needing external trust anchors or computationally expensive systems. PARIOT can be adopted on top of existing state-of-the-art solutions ensuring the widest compatibility with current IoT ecosystems and update frameworks. Also, we have implemented this scheme into PARIOTIC, a prototype to automatically protect C/C++ IoT firmware. The evaluation phase of 50 real-world firmware samples demonstrated the feasibility of the proposed methodology and its robustness against practical repackaging attacks without altering the firmware behavior or severe overheads.