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Notes on security
27 notes, page 1 of 2
Ecology of AI Agents: Collaboration Creates a Population Threshold for Takeoff
The preprint develops a population-level model in which agent fitness depends on cybersecurity capability. It reports that without collaboration, takeoff requires individual capability above a critical threshold; with…
Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints
The preprint proposes the Homomorphic Advantage Operator to stabilize deep reinforcement learning when nonlinear operations are approximated under fully homomorphic encryption. It applies a zero-mean centering…
Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents
The preprint describes artifact-mediated propagation, in which adversarial content inserted into a shared artifact is stored in one assistant's persistent memory, reproduced in a later artifact, and acquired by another…
PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations
PrivDrift is a benchmark for testing whether secrets disclosed during an active conversation remain recoverable after the dialogue shifts to unrelated topics and a later probe uses persuasion. It contains 1,000…
DUMA-Bench: A Dual-Control Multi-Agent Benchmark for Evaluating LLM Agent Security
DUMA-Bench evaluates agent security when both the agent and user can influence shared environment state. It extends tau-squared bench with adversarial environments covering eight vulnerability classes, including RAG…
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This document discusses electronic identification (eID) as a tool for secure access to online services and safer electronic transactions.
Secure all your internal vibe-coded applications — in one click
Cloudflare launched "Access for Workers," letting developers attach login/authentication policies directly to an individual Cloudflare Worker (a unit of deployed code) rather than to a hostname, aimed at securing…
How Cloudflare detects MCP traffic and helps secure it
Cloudflare announced Gateway features that detect Model Context Protocol (MCP) network traffic (the protocol AI agents use to call external tools) and let organizations block or route it through an approved "Portal"…
Hardware Design and Security in the Era of Chiplets and LLMs
The semiconductor industry is experiencing a double revolution with the shift towards heterogeneous 2.5D chiplet systems and the integration of Large Language Models (LLMs) into Electronic Design Automation (EDA)…
Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents
Researchers have developed APPA (Agentic Permissions Policy Algebra), an Information Flow Control (IFC) framework designed to enhance security in Large Language Model (LLM) agents that handle mixed-confidentiality data…
Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security
The paper presents a new benchmark for testing the security of large language model (LLM) agents called 'Adaptive Adversaries'. The benchmark involves 21 scenarios where an adaptive attacker can observe prior defender…
RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control
The paper proposes a system called Real-Time Self-Hosted Computer-Use Agent (RT-SHCUA) for unmanned aerial vehicles (UAVs). This system aims to address the challenges of directly applying SHCUAs to UAV control, which…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The researchers evaluate the performance of language model security agents, focusing on costs rather than just success rates. They use offensive Cybench challenges and defensive Splunk BOTS v1 investigation challenges…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The paper suggests that traditional methods for evaluating security agents, focusing on their offensive capabilities under generous inference budgets, are insufficient as they don't account for the cost of each…
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…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers present Bulkhead, an automated framework that uses large language models and formal methods to detect and fix container escape vulnerabilities called path traversal (PaTra) vulnerabilities. These…
From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure
The paper proposes an agent pipeline that converts natural language system descriptions into OSCAL format for continuous automated compliance management in critical infrastructure. The pipeline uses MCP-grounded…
Distributed Attacks in Persistent-State AI Control
Researchers introduce Iterative VibeCoding, a setting for AI control that studies the deployment of autonomous AI coding agents and their potential for distributed attacks. The study finds that gradual attacks, which…
Distributed Attacks in Persistent-State AI Control
Researchers explore the issue of distributed attacks on autonomous AI coding agents that persistently ship code iteratively. They introduce Iterative VibeCoding, a setting for studying AI control, where an AI agent…
Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains
Researchers introduce Agent Skill Supply Chains (ASSCs) to manage the dependencies of Large Language Model (LLM) agents by characterizing their dependency graphs. The study finds that ASSCs have four structural…
A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems
This research paper discusses the increased role of large language models in various systems beyond just text generation, and how this shift changes the security landscape. The authors systematize the existing…
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
A new routing architecture called ANTAP (Automatic Non-Textual Agent Picker) was introduced in this research. It aims to address security vulnerabilities in multi-agent systems by discarding indirect proxies and…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper introduces a framework called Mesa to prioritize the security of vulnerable communication channels in multi-agent systems (MAS). The framework uses graph-theoretic metrics and dynamic probes to rank which MAS…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper presents Mesa, a framework for prioritizing vulnerable communication channels in multi-agent systems (MAS). Mesa uses graph-theoretic metrics and dynamic probes to identify the edges most likely to affect the…
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