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Notes on cybersecurity
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MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
The paper presents MaliciousSkillBench, a comprehensive benchmark for detecting malicious Agent Skills in large language models (LLMs). The benchmark consolidates various sources of malicious artifacts and reduces them…
VICBench: A Multi-Language Benchmark for Code Vulnerability Detection
The paper introduces VICBench, a multi-language benchmark for code vulnerability detection. It was created by human experts and an agentic workflow, featuring 100 verified vulnerabilities across 88 projects in Python…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The paper proposes a system to secure AI agents' private keys by replacing software-stored keys with hardware-confined ones, accessible through a standard PKCS#11 interface. The system aims to ensure key…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The researchers propose a system that uses hardware-confined keys instead of software-resident keys to enhance security for AI agents performing cryptographic operations. The system aims to protect against key…
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
MemSecBench is a new benchmark for testing the lifecycle security of agent memory systems. The benchmark contains 310 cases drawn from various contexts, and it follows a controlled protocol to test the persistence…
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
The study introduces SecRespond, a new benchmark for evaluating AI agents in post-compromise incident response scenarios. The benchmark tests the ability of agents to produce forensic reports and remediation plans…
Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA
The paper presents a diagnostic framework called FiT (Find before Fine-Tune) to evaluate the suitability of small language models for cybersecurity question-answering tasks. The study finds that fine-tuning these…
Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation
The paper proposes a new approach to penetration testing for AI-enabled systems, focusing on evaluating whether adversaries can manipulate system behavior without directly compromising the underlying infrastructure…
Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation
The paper by Allahbakhsh et al. proposes a new approach to penetration testing for AI-enabled systems, focusing on behavioral objective violation instead of traditional resource compromise. The authors argue that…
Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents
The paper proposes an action-graded harm rubric for evaluating AI agents' behavior during cybersecurity attacks. This scale assesses the severity of an agent's actions based on factors like reversibility, scope…
Agent Data Injection Attacks are Realistic Threats to AI Agents
A new type of attack called Agent Data Injection (ADI) has been introduced, which injects malicious data disguised as trusted data to make AI agents perform unintended actions. This type of attack can bypass existing…
Agent Data Injection Attacks are Realistic Threats to AI Agents
The research paper introduces a new type of attack called Agent Data Injection (ADI), which can manipulate AI agents by injecting malicious data disguised as trusted data. This attack can cause agents to execute…
Distributed Attacks in Persistent-State AI Control
Researchers introduce Iterative VibeCoding, a setting for AI control where an autonomous coding agent builds software over a series of pull requests in a persistent codebase while pursuing a covert side task. The study…
Distributed Attacks in Persistent-State AI Control
A study on distributed attacks in AI control where autonomous coding agents build software over a sequence of pull requests (PRs) in a persistent codebase while pursuing a covert side task. The study found that no…
FLARE-AI: Flaw Reporting for AI
Flare-AI is an open-source system designed for reporting issues in AI systems. It aims to streamline the process of flaw reporting by collecting relevant information through conditional logic and early classification…
COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies
COHORT is an automated end-to-end framework designed to mitigate attacks in enterprise networks, significantly reducing the time and expertise required for this process. It uses a multi-agent workflow, high-fidelity…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
In this research paper titled 'MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems', the authors propose a framework called Mesa to prioritize and secure communication channels in…
Chai: Agentic Discovery of Cryptographic Misuse Vulnerabilities
The paper introduces Chai, an AI-based system that discovers and validates cryptographic misuse vulnerabilities using naturally occurring signals. Instead of auditing one codebase for many flaws, Chai catalogs flaws at…
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
The paper introduces ShareLock, a stealthy multi-tool threshold poisoning framework designed to exploit the interaction between Large Language Models (LLMs) and external tools through the Model Context Protocol (MCP)…
Detecting Malicious Agent Skills in the Wild using Attention
The research presents a two-stage detector called Locate-and-Judge for identifying malicious skills in Language Model (LLM) agents. These skills are third-party packages of instructions that can potentially exfiltrate…
Detecting Malicious Agent Skills in the Wild using Attention
The researchers present Locate-and-Judge, a two-stage detector designed for malicious agent skills in LLM (Language Model) systems. It reduces the cost of auditing an entire marketplace by focusing on high-attention…
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