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Agentic AI Security Test Harness
84 Signals

Agentic AI Security Test Harness

A SaaS platform that continuously tests LLM apps, agents, MCPs, and AI workflows for prompt injection, data leakage, and adversarial abuse.

Added Jun 9, 2026

AI Security
Application Security
Developer Tools
Opportunity score

Medium opportunity (67%)

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The Problem

Companies building LLM-backed applications are hiring specialized security engineers to manually assess prompt injection, model abuse, data leakage, and risks across agents and AI stack components. These threats are new, fast-moving, and hard to validate with traditional application security tools, especially when systems are non-deterministic and connected to enterprise data or tools.

Potential Solution

The product provides automated offensive testing for AI-enabled systems, including red-team scenario generation, prompt-injection probes, agent abuse simulations, data-exfiltration checks, and model/API boundary testing. It produces actionable risk reports, regression tests, and trust-layer policy recommendations that security and AI engineering teams can run before launch and continuously in CI/CD.

Why Now?

Enterprises are rapidly deploying LLM APIs, self-hosted models, agents, MCPs, and AI-powered features while job postings show a clear need for dedicated AI security expertise. Security teams need repeatable tooling because hiring offensive AI security specialists does not scale across every product team and release cycle.

Market validation
Search demand

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Competition (0)

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Showing 1-20 of 84 signals

Job adsSep 17, 2026
gruve
Security Consultant II

Collaborate with Blue Teams during purple-team exercises to validate and improve detection and response capabilities. Perform security testing of AI/ML and LLM-based applications, including prompt injection, jailbreak, model extraction and adversarial-input testing.

Job adsSep 17, 2026
gruve
Security Consultant II

Perform security testing of AI/ML and LLM-based applications, including prompt injection, jailbreak, model extraction and adversarial-input testing. Apply relevant AI security frameworks such as OWASP Top 10 for LLM Applications and MITRE ATLAS to identify AI-specific risks.

PodcastsSep 3, 2026
HiddenLayer nabs $100M as enterprises rush to secure their AI deployments; plus, AfterQuery reportedly becomes Y Combinator’s fastest-ever unicorn
TechCrunch Startup News
S2

The Austin-based startup broadly still sells what it used to in 2023, but Sestito told TechCrunch that the biggest change it's had to make was to extend its existing products discovery, runtime protection, attack simulation, and supply chain security to address prompt injection, agent manipulation, and malicious tool use. Sestito said inference is still inference, so whether it's on a traditional machine learning model, whether it's Gen AI, whether it's an agentic work stream, a lot of our technology still applied. So really, we haven't had to pivot, but we've had to grow our scope from traditional modeling to Gen AI to agentic. Sestito addressed that runtime security has especially become a priority as AI deployments grow common across businesses and likened it to traditional endpoint detection and response solutions, but specifically for AI.

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