Parth, Co-founder•September 26, 2025•8 min read

Why Ad-hoc Red Teaming Fails for AI Agents

Testing Strategy

Security Gaps

AI agents are rapidly finding their way into enterprises—automating workflows, handling sensitive data, and even making autonomous decisions. With this power comes a significant responsibility: securing them from attacks that exploit their reasoning, memory, or integrations with external systems.

Many organizations today attempt ad-hoc red teaming—running improvised attack simulations—to check the robustness of their AI agents. Unfortunately, this approach consistently fails. Let's unpack why.

The Problem with Ad-hoc Red Teaming

Ad-hoc Red Teaming Problems Flow Diagram

Too Narrow in Scope

High

Ad-hoc tests focus on a few well-known vulnerabilities. They rarely cover the full spectrum of attacks possible in real-world deployments, like multi-step prompt injection, tool abuse, or lateral movement across integrated systems.

One-Time Effort, Short Shelf Life

High

AI agents evolve rapidly. Updating their capabilities or changing their integrations often invalidates prior tests. Ad-hoc red teaming doesn't scale with this pace.

Coupled to Specific Frameworks

Medium

Tests are often hard-coded to a specific agent framework (LangChain, AutoGen, etc.), making them non-transferable. That means every new setup requires starting from scratch.

Gaps in Threat Modeling

Critical

Without structured threat models, ad-hoc testing tends to miss nuanced risks such as data exfiltration through subtle prompt manipulations or poisoning attacks.

False Confidence

A few successful tests can create the illusion of safety. But without fine-grained, repeatable coverage, organizations are blind to systemic risks.

The Need for Fine-Grained, Continuous Security

Ad-hoc Red Teaming vs Fine-grained Security Assessment Comparison

Securing AI agents requires:

Realistic attack simulations

Repeatability across frameworks

Coverage across multiple layers (reasoning, memory, integrations, APIs)

Continuous adaptability to evolving threats

This is where structured, modular approaches shine.

Introducing Vigilis: Fine-Grained Security for AI Agents

Vigilis solves the limitations of ad-hoc red teaming

A plug & play, fine-grained security assessment platform built for AI agents:

🔧

Modular

Attacks are decoupled from the details of the agentic framework, making testing reusable across any setup.

⚙️

Configurable

Flexible enough to adopt different threat models and a variety of attack types (prompt injection, data exfiltration, tool misuse, poisoning, etc.).

🔄

Extensible

Scales across frameworks, ensuring you're not locked into a single ecosystem.

With Vigilis, enterprises can perform continuous, real-world-like security testing that adapts as AI agents evolve—ensuring these agents remain assets, not liabilities.

Key Takeaway

Ad-hoc red teaming may give quick wins, but it fails to provide lasting security assurance. Vigilis delivers a fine-grained, modular, configurable, and extensible solution that helps organizations harden AI agents against today's and tomorrow's threats.

Ready to Secure Your AI Agents?

Don't let ad-hoc testing create false confidence. Get comprehensive, continuous security assessment for your AI agents with Vigilis.