Mend AI

Overview

Securing modern applications that integrate AI introduces new challenges that traditional AST tools weren’t built to address. While these tools remain essential for identifying many classes of vulnerabilities, they often miss the unique risks posed by today’s AI-driven applications—including vulnerabilities in third-party AI components, AI agents, and custom LLM implementations, where behavior can shift dynamically based on prompts, context, and model updates.

AI also brings a growing set of compliance and governance concerns—from emerging regulations and license constraints to data usage obligations hidden in providers’ terms of service. These factors introduce a new layer of risk that security teams must address alongside their existing AppSec workflows.

Why Mend AI

The Challenge: AI is Transforming Applications—and Application Security

The rapid adoption of AI is fundamentally changing how applications are built. Organizations are embedding AI models, integrating LLM APIs, deploying agents, and building RAG pipelines at an unprecedented pace. While this innovation drives productivity and competitive advantage, it creates a security crisis that traditional AppSec tools simply weren't built to handle.

The problems are clear:

Invisibility Crisis: Most organizations have no idea which AI models, agents, system prompts, RAG pipelines, and MCPs are actually running in their applications. Unlike traditional dependencies, these AI elements operate in the shadows—creating blind spots that traditional SCA and SAST tools can't detect. This "Shadow AI" phenomenon means teams are managing risks they can't even see.

Unique Attack Surface: AI components don't just have traditional vulnerabilities. They introduce entirely new categories of risk—prompt injection attacks, context leakage, data exfiltration through LLM conversations, jailbreaks, hallucinations, and model poisoning. Each AI interaction with your application is a potential security event that behaves unpredictably and non-deterministically.

Governance Vacuum: AI models and frameworks bring complex licensing issues, regulatory compliance requirements, and ethical considerations. Without visibility and control, organizations face compliance violations, intellectual property exposure, and regulatory penalties—especially as frameworks like the EU AI Act and industry-specific regulations tighten their requirements.

The Traditional AppSec Gap: Existing security tools focus on vulnerabilities in AI-generated code, but they completely miss the AI components themselves—the models, agents, system prompts, agent tools, MCPs, and RAG systems that power modern applications.

Our Mission: Secure AI-Driven Systems as part of the Development Cycle

At Mend, we believe AI security shouldn't require organizations to abandon everything they've built. Our mission is to extend proven application security methodologies to AI components, leveraging the same risk management strategies, processes, and automation workflows that enterprises already trust.

We prioritize securing the AI elements themselves—the models, agents, system prompts, RAG pipelines, and MCPs—not just the code they help generate, because that's where the unique risks live.

Our Approach: Shift-Left AI Security, Integrated Into Your Workflow

Unlike isolated point solutions that create security silos, Mend AI provides a comprehensive, platform approach that catches AI vulnerabilities before they hit production:

1. Detects What You Can't See: By scanning your repositories, we automatically discover all AI models, agents, frameworks, system prompts, RAG pipelines, MCPs, and agent tools across your codebase with continuous AI-BOM (AI Bill of Materials) generation. Bring Shadow AI into the light before deployment.

2. Assesses Real-World Risk: Go beyond static analysis with behavioral testing (red teaming) that simulates adversarial attacks unique to your applications, uncovering how your AI systems actually behave under threat—including prompt injection, bias, hallucinations, and data leaks.

3. Enforces Governance at Scale: Apply and enforce policies for model usage, licensing compliance, prompt safety, and risk thresholds using Mend's robust policy engine and automation workflows throughout the SDLC—catching issues in development, not production.

4. Integrates Seamlessly: Security that slows down development fails. Mend AI integrates into existing workflows—CI/CD pipelines, IDEs, and development tools—scanning repositories where developers work for maximum efficiency and minimal disruption.

5. Provides One Platform for Everything: Secure your entire application—traditional code, open source dependencies, containers, and all AI elements (models, agents, system prompts, RAG systems, MCPs)—through a single, unified platform. No more juggling multiple tools or vendors.

Why This Matters Now

With 33% of organizations already using generative AI in production applications and that number growing rapidly, the window to establish proper AI security governance is closing. Organizations that wait will face:

• Data breaches through unmonitored AI interactions

• Regulatory penalties as AI-specific legislation tightens globally

• Intellectual property exposure through unauthorized model usage

• Reputational damage from biased or hallucinated AI outputs

• Uncontrolled costs from Shadow AI proliferation

Mend AI empowers organizations to innovate with AI confidently—moving from reactive vulnerability chasing to proactive risk management that catches issues in your repositories before they reach production, using security practices that scale with your AI adoption.

Getting started with Mend AppSec Platform

Mend AI detection

Risks in AI components

Behavioral Risks (Red-Teaming)

Release Notes

• Mend AI Release Notes

• Mend Repository Integrations and CLI Release Notes