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SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning

ai-safety › robustness

📄 Abstract

Abstract: The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring their safe deployment. We present SIRAJ: a generic red-teaming framework for arbitrary black-box LLM agents. We employ a dynamic two-step process that starts with an agent definition and generates diverse seed test cases that cover various risk outcomes, tool-use trajectories, and risk sources. Then, it iteratively constructs and refines model-based adversarial attacks based on the execution trajectories of former attempts. To optimize the red-teaming cost, we present a model distillation approach that leverages structured forms of a teacher model's reasoning to train smaller models that are equally effective. Across diverse evaluation agent settings, our seed test case generation approach yields 2 -- 2.5x boost to the coverage of risk outcomes and tool-calling trajectories. Our distilled 8B red-teamer model improves attack success rate by 100%, surpassing the 671B Deepseek-R1 model. Our ablations and analyses validate the effectiveness of the iterative framework, structured reasoning, and the generalization of our red-teamer models.

Authors (4)

Kaiwen Zhou

Ahmed Elgohary

A S M Iftekhar

Amin Saied

Submitted

October 30, 2025

arXiv Category

cs.CR

arXiv PDF

Key Contributions

Introduces SIRAJ, a generic red-teaming framework for LLM agents that plans and invokes tools. It employs a dynamic process to generate diverse test cases covering various risks and iteratively refines attacks. A key innovation is model distillation using structured reasoning to train smaller, effective red-teaming models, optimizing cost.

Business Value

Significantly enhances the safety and reliability of AI agents by proactively identifying and mitigating potential vulnerabilities before deployment, reducing risks of misuse or unintended consequences.

Paper Metadata

Innovation Type

Novel Framework and Distillation Technique

Deployment Feasibility

Feasible as a framework and methodology for AI safety teams and researchers. Requires expertise in LLMs, adversarial attacks, and potentially model distillation.

Limitations Addressed

New safety risks introduced by LLM agents' ability to plan and invoke tools,Need for comprehensive red-teaming systems,High cost of red-teaming

Performance Gains

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Technical Tags

red-teamingLLM agentstool-usesafety risksvulnerability discoveryadversarial attacksmodel distillationstructured reasoningblack-box agentsrisk outcomes

Research Topics

AI SafetyLLM SecurityAdversarial AIAI AgentsRobustness Testing

Methods & Architectures

dynamic two-step red-teaming processseed test case generationmodel-based adversarial attacksmodel distillation for efficiencystructured reasoning distillation LLM agentsdistilled models

Applications & Tasks

AI Safety Cybersecurity LLM Development AI Agent Deployment Ensuring safety of LLM agentsDiscovering vulnerabilitiesEfficient red-teaming red-teaming arbitrary black-box LLM agentsgenerating diverse seed test casesconstructing iterative adversarial attacksdistilling reasoning for efficient red-teaming

Related Fields

AI SafetyCybersecurityMachine LearningNatural Language ProcessingAdversarial Machine Learning

Keywords

red teamingLLM agentstool useAI safetyvulnerabilitiesadversarial attacksmodel distillationstructured reasoningblack-boxSIRAJrobustness

Academic Context

#AI Safety#LLM Security#Adversarial AI#AI Agents#Robustness Testing

Commercial Potential

Potential Products

Automated red-teaming platforms for LLM agentsVulnerability assessment services for AI systems

Target Industries

TechnologyCybersecurityFinanceGovernmentAny industry deploying LLM agents

Use Case Examples

Testing an LLM agent's ability to avoid generating harmful content when using tools.Identifying ways an LLM agent could be tricked into performing unauthorized actions.Ensuring an LLM agent's tool usage is robust against unexpected inputs.

Competitive Edge

Provides a more efficient and comprehensive approach to red-teaming LLM agents compared to manual methods or simpler automated testing, particularly through its distillation technique.

Market Opportunity

Growing rapidly, as AI safety and security become paramount for LLM deployment.

Revenue Models

Service contracts for red-teaminglicensing of the SIRAJ platformconsulting.

Resource Requirements

Compute Needs

High for training distilled models, moderate for running red-teaming attacks.

Data Requirements

Agent definitions, diverse seed test cases, execution logs.

Deployment Constraints

Access to target LLM agent (black-box),Computational resources for attack generation,Defining comprehensive risk outcomes

Scalability

Scalable through the use of distilled models and automated attack generation.

Regulatory Considerations

Ethical guidelines for red-teamingResponsible disclosure of vulnerabilities

Production Readiness

Maturity Level

Research/Prototype

Time to Market

2-4 years

Patent Potential

High, for the red-teaming framework and the model distillation technique for adversarial purposes.

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