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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Security Smells in Ansible and Chef Scripts: A Replicatio...
Akond Rahman, Md. Rayhanur Rahman, Chris Parnin, Laurie Williams · 2019-07-17 · via cs.CR updates on arXiv.org

Context: Security smells are recurring coding patterns that are indicative of security weakness, and require further inspection. As infrastructure as code (IaC) scripts, such as Ansible and Chef scripts, are used to provision cloud-based servers and systems at scale, security smells in IaC scripts could be used to enable malicious users to exploit vulnerabilities in the provisioned systems. Goal: The goal of this paper is to help practitioners avoid insecure coding practices while developing infrastructure as code scripts through an empirical study of security smells in Ansible and Chef scripts. Methodology: We conduct a replication study where we apply qualitative analysis with 1,956 IaC scripts to identify security smells for IaC scripts written in two languages: Ansible and Chef. We construct a static analysis tool called Security Linter for Ansible and Chef scripts (SLAC) to automatically identify security smells in 50,323 scripts collected from 813 open source software repositories. We also submit bug reports for 1,000 randomly-selected smell occurrences. Results: We identify two security smells not reported in prior work: missing default in case statement and no integrity check. By applying SLAC we identify 46,600 occurrences of security smells that include 7,849 hard-coded passwords. We observe agreement for 65 of the responded 94 bug reports, which suggests the relevance of security smells for Ansible and Chef scripts amongst practitioners. Conclusion: We observe security smells to be prevalent in Ansible and Chef scripts, similar to that of the Puppet scripts. We recommend practitioners to rigorously inspect the presence of the identified security smells in Ansible and Chef scripts using (i) code review, and (ii) static analysis tools.