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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
Static Application Security Testing of Consensus-Critical...
Jasper Surmont, Weihong Wang, Tom Van Cutsem · 2023-08-21 · via cs.CR updates on arXiv.org

Blockchains require deterministic execution in order to reach consensus. This is often guaranteed in languages designed to write smart contracts, such as Solidity. Application-specific blockchains or ``appchains'' allow the blockchain application logic to be written using general-purpose programming languages, giving developers more flexibility but also additional responsibilities. In particular, developers must ensure that their blockchain application logic does not contain any sources of non-determinism. Any source of non-determinism may be a potential source of vulnerabilities. This paper focuses on the use of Static Application Security Testing (SAST) tools to detect such sources of non-determinism at development time. We focus on Cosmos, a prominent open-source project that lets developers build interconnected networks of application-specific blockchains. Cosmos provides a Software Development Kit (SDK) that allows these chains to be implemented in the Go programming language. We create a corpus of 11 representative Cosmos-based appchains to analyze for sources of non-determinism in Go. As part of our study, we identified cosmos-sdk-codeql, a set of CodeQL code analysis rules for Cosmos applications. We find that these rules generate many false positives and propose a refactored set of rules that more precisely detects sources of non-determinism only in code that runs as part of the blockchain logic. We demonstrate a significant increase in the precision of the rules, making the SAST tool more effective and hence potentially contributing to enhanced security for Cosmos-based blockchains.