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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
An investigation of security controls and MITRE ATT\&...
Md Rayhanur Rahman, Laurie Williams · 2022-11-12 · via cs.CR updates on arXiv.org

Attackers utilize a plethora of adversarial techniques in cyberattacks to compromise the confidentiality, integrity, and availability of the target organizations and systems. Information security standards such as NIST, ISO/IEC specify hundreds of security controls that organizations can enforce to protect and defend the information systems from adversarial techniques. However, implementing all the available controls at the same time can be infeasible and security controls need to be investigated in terms of their mitigation ability over adversarial techniques used in cyberattacks as well. The goal of this research is to aid organizations in making informed choices on security controls to defend against cyberthreats through an investigation of adversarial techniques used in current cyberattacks. In this study, we investigated the extent of mitigation of 298 NIST SP800-53 controls over 188 adversarial techniques used in 669 cybercrime groups and malware cataloged in the MITRE ATT\&CK framework based upon an existing mapping between the controls and techniques. We identify that, based on the mapping, only 101 out of 298 control are capable of mitigating adversarial techniques. However, we also identify that 53 adversarial techniques cannot be mitigated by any existing controls, and these techniques primarily aid adversaries in bypassing system defense and discovering targeted system information. We identify a set of 20 critical controls that can mitigate 134 adversarial techniques, and on average, can mitigate 72\% of all techniques used by 98\% of the cataloged adversaries in MITRE ATT\&CK. We urge organizations, that do not have any controls enforced in place, to implement the top controls identified in the study.