Automated Red-Teaming Framework for LLM Agent Systems

Overview

As LLM-based agents become increasingly deployed in critical applications, from code generation to autonomous web browsing and computer control, the need for comprehensive security evaluation has never been more urgent. We propose developing an automated red-teaming framework that systematically explores vulnerabilities across diverse agent architectures, attack surfaces, and threat models. This framework will enable proactive identification of security risks before they can be exploited in production environments.

Problem Statement

Our framework will target various agent classes, e.g., coding agents, web agents, computer use agents, and general assistant agents. Each agent type presents unique attack surfaces: coding agents may generate malicious code, web agents can perform unauthorized actions, and computer use agents might leak sensitive data or hijack system resources. The framework will address two primary attack goal categories: system-level threats (availability disruption, data poisoning, resource hijacking) and user-level threats (malicious code injection, unauthorized actions, privacy violations).

We will develop red-teaming capabilities across different threat model scenarios. Whitebox adversaries have full access to architecture, parameters, and internal states of models and agents, enabling gradient or source-level attacks. Greybox adversaries receive partial signals (e.g., logits, intermediate representations, or agent trajectories) reflecting insider threats. Blackbox adversaries are restricted to queries and observable outputs, simulating external attackers. This graduated structure ensures the framework can rigorously evaluate agents against adversaries with varying levels of system knowledge.

Methodology

We will first develop a comprehensive toolkit of atomic red-teaming capabilities targeting specific vulnerabilities across different threat models. For whitebox scenarios, we will implement gradient-based token optimization and model weight analysis to identify exploitable patterns. For greybox settings, we will create logits and trajectory analysis tools and scoring functions that leverage partial system feedback. For blackbox scenarios, we will build query-based probing modules using evolutionary search, prompt injection libraries with known attack patterns, and behavioral analysis tools. Each tool will feature modular interfaces for attack generation, evaluation metrics, and logging capabilities.

These atomic tools will be orchestrated through an intelligent meta-agent that reasons about attack strategy composition. The meta-agent maintains a dynamic repository of red-teaming tools and employs LLM-based reasoning to analyze target architectures, hypothesize vulnerabilities, and compose multi-stage attacks. It iteratively proposes attack combinations, scores their effectiveness, selects Pareto-optimal candidates balancing success rate and cost, and refines strategies through reinforcement learning. This agentic approach discovers novel attack vectors by reasoning about system structure, adapts based on intermediate results, and maintains diverse attack portfolios.

Expected Outcome

This framework will provide unprecedented visibility into LLM agent security, enabling developers to identify and remediate vulnerabilities before deployment. We will develop state-of-the-art red-teaming methods, and design flexible frameworks to incorporate diverse red-teaming techniques. Integrated with AgentBeats evaluation platform, it delivers comprehensive security assessment across a wide range of agents with automated, continuous testing. Ultimately, this work will establish best practices for secure agent development and contribute to the broader goal of trustworthy AI systems.

As AIs evolve into agentic systems with tool use and computer control capabilities, they create unprecedented security vulnerabilities that could be exploited at scale. This project advances AI safety by systematically identifying these vulnerabilities before deployment, enabling proactive remediation and establishing security-by-design practices for agent development. As transformative AI emerges through agentic architectures that autonomously pursue goals and interact with real-world infrastructure, vulnerabilities pose existential risks through misuse or adversarial exploitation. This work provides essential framework for understanding and mitigating these risks before agents reach transformative capability levels, ensuring powerful AI systems remain secure and aligned with human values.

Mentees will lead the design and implementation of core framework components, including the atomic red-teaming toolkit and meta-agent orchestration system. They will make key architectural decisions about tool interfaces, attack generation methods, and evaluation strategies across different threat models. Mentees will drive experimental design, conduct empirical evaluations against agent benchmarks, and iterate on the framework based on results. While mentors provide guidance on research direction and technical challenges, mentees will have substantial autonomy in implementation choices, problem-solving approaches, and contributing novel extensions to the framework's capabilities.

• Highly proficient in Python and modern ML frameworks (e.g., pytorch, transformers)
• Prior experience building or working with LLM-based agents (e.g., Google ADK, OpenAI Agents SDK)
• Familiarity with adversarial ML or security concepts (e.g., prompt injection, jailbreaking, system security)