Vijil DART: AI Agent Security Testing & Policy Violation Detection

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Vijil has introduced Diamond Adaptive Red Teaming for Agents (DART), a system designed to identify security vulnerabilities and policy violations within enterprise AI agents. The tool utilizes self-generated adversarial agents to conduct multi-turn attacks that evolve tactics across interactions, episodes, and engagements. This approach allows AI developers and security engineers to detect a broader range of issues across their agent fleets compared to traditional red-team methods that rely on static test prompts.

Gartner projects that the average Fortune 500 company will deploy over 150,000 AI agents by 2028

However, AI engineering and governance teams face significant challenges in manually testing such large-scale deployments due to resource constraints. Simultaneously, threat actors are leveraging their own AI agents to execute prolonged, multi-turn attacks against enterprise systems. Conventional red-teaming tools struggle to keep pace, as they are limited to predefined attack patterns and often operate outside the development lifecycle, providing delayed feedback that is difficult to address before deployment.

DART addresses these limitations by enabling comprehensive testing of AI agents

The system employs adaptive attacks that adjust to the target agent’s environment rather than relying on fixed scripts. It combines scalability with stealth to generate sustained multi-turn attacks that probe and exploit weaknesses. The tool autonomously identifies vulnerabilities without explicit instructions, iteratively refining its tactics based on the agent’s responses.

Key features of DART include customizable risk coverage

Customizable risk coverage integrates predefined taxonomies from OWASP, MITRE, and Vijil research, while also supporting enterprise-specific risk catalogs. Adaptive attack chaining allows the tool to sequence attacks across interactions, expanding the attack surface and exploiting weaknesses. Developer-friendly tooling is available via plugins for coding agents like Claude Code and Codex, ensuring compatibility with any agent framework or deployment platform.

In a benchmark evaluation using the DecodingTrust-Agent dataset

DART achieved an attack success rate 1.5 times higher than its closest competitor. It outperformed the competitor in nine out of twelve enterprise agent tasks across domains such as CRM, code generation, customer service, medical, research, and travel.

“Custom AI agents that access sensitive data and make autonomous decisions require a tailored approach to quality assurance,” stated Vin Sharma, CEO of Vijil. “There is a critical gap between agents that appear functional in demos and those that demonstrate reliability, security, and safety under pressure. DART bridges this gap, reducing manual red-teaming efforts by weeks and saving tens of thousands of dollars compared to traditional methods.”

DART is part of the Vijil Diamond platform

Once DART identifies vulnerabilities, other platform modules perform root-cause analysis, implement policy-driven safeguards, and recommend code fixes. The Vijil platform includes modules for shadow AI discovery, pre-deployment flaw detection, production compliance, and continuous agent improvement. Vijil Discover identifies and catalogs shadow AI agents, while Vijil Diamond evaluates agents before deployment. Vijil Dome ensures adherence to organizational policies in live environments, and Vijil Darwin enhances agent resilience as new threats and user behaviors emerge.

Together, these components ensure AI agents remain robust against adversarial challenges

By embedding resilience into AI agents, the Vijil platform provides a comprehensive solution for security, compliance, and continuous improvement. DART’s adaptive testing capabilities, combined with the platform’s modular architecture, offer enterprises a scalable and effective way to address the growing complexity of AI deployments.