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AI Application Security

AI Code Security for What Your Team Ships With AI

Developers ship code and agents with Claude Code, Codex and Cursor faster than a security team can review it. Traditional AppSec tools scan for known vulnerabilities; they can’t reason about prompt-driven logic, agent behavior, or what happens when your AI application calls APIs, retrieves data, and acts for users. Trent reads the code and configuration, maps how it fits together, traces the attack chains your scanners miss, and proposes fixes. A person approves every change.

Trent capability graph with an open attack chain and the ranked threats list for an AI application

The gap

Your AppSec Tools Scan Code. Your AI App Makes Decisions.

Traditional tools focus on scanning code after it’s written, catching CVEs, flagging insecure dependencies, spotting known patterns, but that’s not the same as understanding whether your AI application is actually secure. The gap between “no known code vulnerabilities” and “secure AI application” is where risk compounds.

  • Your AI App Has an Attack Surface Your Tools Can’t See

    A single LLM call can access external data, call third-party services, modify databases, and trigger downstream actions, all from a single prompt. Prompt injection, tool misuse, data exfiltration through agent chains, your SAST/DAST tools are blind to all of it.

  • New Threat Surfaces You Can’t See With Existing Tools

    Traditional scanners, firewalls, and SAST/DAST tools are blind to the threats agentic systems create because they were designed for a world where software does exactly what it’s told. Your AI application reasons, plans, and acts; different threat model entirely.

  • Your Agents Have More Power Than You Intended

    Function calling and tool access give your AI application capabilities your security team didn’t explicitly approve. An agent with “update contact records” permission can be manipulated into deleting them. The permission was granted, nobody checked whether the action was appropriate.

How it works

Multiple Agents. One Continuous Loop. Applied to Your AI Application.

Trent’s specialized agents work in continuous loops across your environment: Scanning Agents find what matters, Judging Agents prioritize based on real risk, Mitigation Agents resolve issues and validate fixes, and Evaluation Agents assess your overall posture and forecast where risk will emerge next. Each cycle compounds intelligence, so your security gets sharper, faster, and more precise over time.

  1. Scan

    This agent analyzes your code, agent definitions, and system architecture to develop a deep understanding of your AI application and its workflows. It finds the threats that matter in your specific environment.

    Trent service architecture map and asset inventory of an AI applicatio
  2. Judge

    Not everything the scan finds is a real problem. The judgment agent prioritizes based on real risk rather than static rules. That means you focus on what actually matters for your application.

    Trent threats ranked by severity with the threat report summary
  3. Mitigate

    Trent helps you tackle identified threats with clear plans, detailed tasks, and ready-to-use remediation guidance your team can act on immediately.

    Trent security requirements with automation approval and the remediation task list
  4. Evaluate

    As new agents are deployed or code changes and as your architecture evolves, Trent continuously re-scans, re-judges, re-mitigates, and re-evaluates, keeping your security current without a single human in the loop. Each cycle builds on the last.

    Trent attack chains and asset risk distribution report

Get started

Connect Your AI Application.

  1. Connect

    Connect your source code repository, agent definitions, or design documents. Trent begins analyzing your AI application’s architecture and workflows.

  2. Assess

    Receive a prioritized security assessment grounded in your specific application, its AI components, and your business requirements. Provide additional context at any time to sharpen the analysis.

  3. Evolve

    As you ship new features, update models, or connect new tools, Trent continuously re-scans, re-judges, re-mitigates, and re-evaluates. Your security stays current without manual intervention.

Integrations

Works With Your AI Stack

  • Claude Code
  • Codex
  • Cursor
  • OpenClaw
  • GitHub
See all supported connection paths

Next step

See What Your AppSec Tools Miss.

Your AI application has risks your existing security stack was never designed to find. Connect your repo and find out.

FAQs

What is AI application security?

AI code security is the practice of reviewing code and agents written with AI coding tools for the flaws scanners miss: architecture, business logic, and what the agents are allowed to do.

How is Trent different from traditional AppSec tools?

Traditional tools scan code for known vulnerabilities. They can’t reason about agent behavior, multi-step autonomy, or the new threat surfaces your AI application creates. Trent’s agents work in continuous loops, scanning, judging, mitigating, and evaluating, to develop a deep understanding of your system architecture and agentic workflows.

What kinds of threats does Trent address?

AI agent security is about what an agent can do, not only what its code says: which tools it can call, what data it can reach, and what happens when untrusted input steers it. The new categories of risk that agentic systems introduce: prompt injection, tool misuse, unintended autonomous actions, data exfiltration through agent chains, and privilege escalation across interconnected agents. These align with the OWASP Top 10 for LLM Applications.

How does this relate to the OWASP Top 10 for LLMs?

The threats Trent addresses align with the OWASP Top 10 for LLM Applications. Trent prioritizes based on the actual impact to your specific application and environment, not just by OWASP category.

Do I need to give Trent access to my source code?

You can start with a URL-only assessment that analyzes your application from the outside. For deeper analysis, connect your source code repository, agent definitions, or design documents. You can also provide additional context at any time, design documents, product specifications, compliance requirements, or technical constraints to sharpen the assessment further. You control what level of access to provide.

How is this different from AI Security Posture Management (AI-SPM)?

AI-SPM manages your security posture across all your AI systems over time. AI Application Security secures the specific AI application you’re building. Think of AI-SPM as the ongoing view across your estate. AI Application Security as the assessment of the specific application you’re shipping.

Get secure.