PLATFORM

Automated AI Red Teaming

[AUTOMATED ADVERSARIAL TESTING]

Continuous, context-aware red-teaming campaigns that model your full agent system—tools, permissions, and interactions—before attackers do. If there's an exploit chain, we surface it first.

Our in-house attacker model ingests runtime context, maps the tool and permission graph, and generates multi-step exploits that target seams between components. Findings include reproduction traces, prioritized fixes, and the runtime controls derived from red-team evidence.

OVERVIEW

Automated AI Red Teaming

Our automated adversarial testing platform (code-named REDit) is a context-aware attacker model that ingests your runtime environment, maps tool and permission graphs, and generates multi-step exploits across components. It focuses on system-level risks that isolated agent tests miss, then turns the findings into prioritized fixes and runtime controls.

Context-Aware Attacker Model

Our in-house attacker model digests the live environment and builds a tool + permission graph so it can target seams between components, not just single prompts.

Interaction-Effect Exploits

Multi-step attack chains probe how agents, tools, and policies interact, revealing exploit paths that black-box or single-agent testing never finds.

In-House RL Environment

We train red-team attackers inside a dedicated RL environment to continuously discover new system risks as your workflows and models evolve.

Vulnerability Forecasting

We distill red-team procedures into forecasting signals that flag risky diffs in code, prompts, or tool graphs—lightweight enough for CI/CD.

Operational Outputs

Each exploit ships with reproduction steps, attack traces, and recommended runtime controls so teams can harden workflows quickly.

DEMO

Red Teaming Demo

Vulnerability tagging and prioritization dashboard

Policy-Aligned Red Teaming

Tag attack vectors to OWASP, NIST, SOC 2, or custom policies so remediation work streams automatically receive context.

Library of 60+ automated adversarial attack algorithms

Attack Algorithm Library

Browse 60+ automated adversarial algorithms with filters for single-turn, multi-turn, and obfuscation techniques.

Tree of Attacks with Pruning analytics dashboard

TAP Tree Explorer

Inspect every branch that Tree of Attacks with Pruning evaluated, including pruning decisions and replayable transcripts.

Crescendo Multi-Turn Attack analytics dashboard

Crescendo Multi-turn Campaign Analytics

Watch Crescendo escalate from benign prompts to policy violations while tracking success curves by guardrail and account.

Emoji Obfuscation Attack Analytics

Live pulse board sumarrizes emoji obfuscation attack success rates, and severity signals at a glance.

Flip Obfuscation Attack Analytics

Side-by-side comparison of safe vs compromised responses from the flip attack so engineers can see exactly how guardrails were bypassed.

Translate Obfuscation Attack Analytics

Dive into each jailbreak transcript showing timestamps, prompts, model outputs, and affected policies.

PROCESS

How It Works

Ingest System Context

Connect agents, repos, and toolchains so the attacker model can learn your runtime environment and permission graph.

Generate Exploit Chains

Run context-aware campaigns that probe interaction effects across agents and tools using 60+ algorithms and 100k+ scenarios.

Forecast & Harden

Distill findings into vulnerability forecasting and runtime controls so risky changes are caught in CI/CD.

ALGORITHMS

Red Teaming Algorithms

Our proprietary red-team agent is trained using unsafety-tuned reinforcement learning inside a dedicated RL environment, enabling it to uncover system-level failure modes across agent toolchains.

With 60+ jailbreak algorithms and 100,000+ adversarial scenarios, we cover multi-turn exploits, obfuscation tactics, and cross-tool attack chains.

GCG (Greedy Coordinate Gradient)

High RiskSingle-turn

A gradient-based optimization attack that generates adversarial suffixes to bypass safety mechanisms. GCG uses gradient information to automatically craft token sequences that trigger harmful outputs, achieving high success rates with transferability across models.

Key Features

Automatic suffix generation
Cross-model transferability
Gradient-based optimization

Crescendo

Very High RiskMulti-turn

A sophisticated multi-turn jailbreak that starts with benign queries and gradually escalates to harmful content. Exploits LLMs' tendency to follow conversational patterns and focus on their own generated text, making it highly effective and difficult to detect.

Key Features

Multi-turn escalation
Context manipulation
Pattern exploitation

TAP (Tree of Attacks with Pruning)

High RiskMulti-turn

An iterative refinement approach using tree search with intelligent pruning. TAP systematically explores multiple attack variations, pruning ineffective branches to efficiently find successful jailbreaks while minimizing query count.

Key Features

Tree-based exploration
Intelligent pruning
Iterative refinement

AutoDAN

Very High RiskSingle-turn

Employs genetic algorithms to evolve semantically meaningful jailbreak prompts. Unlike gradient-based methods, AutoDAN generates fluent, human-readable prompts that bypass perplexity filters while maintaining high attack success rates.

Key Features

Genetic optimization
Semantic coherence
Stealth capabilities

WHY

Why AI Red Teaming?

Make red-teaming the foundation for vulnerability forecasting and runtime controls in every release.

Targets realistic system risks so teams are not stuck chasing isolated prompt hacks.

Finds and reproduces multi-step exploits early so fixes land before deployment.

Builds a data flywheel for vulnerability forecasting, enabling CI/CD evaluation now and endpoint detection for AI agents over time.

GET STARTED

Ready to stress-test your AI systems?

Join leading organizations using context-aware red teaming to identify and fix system vulnerabilities before they become security incidents.

Start Red Teaming

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ALGORITHMS

Red Teaming Algorithms

Our proprietary red-team agent is trained using unsafety-tuned reinforcement learning inside a dedicated RL environment, enabling it to uncover system-level failure modes across agent toolchains.

With 60+ jailbreak algorithms and 100,000+ adversarial scenarios, we cover multi-turn exploits, obfuscation tactics, and cross-tool attack chains.

GCG (Greedy Coordinate Gradient)

High RiskSingle-turn

Key Features

Automatic suffix generation
Cross-model transferability
Gradient-based optimization

Crescendo

Very High RiskMulti-turn

Key Features

Multi-turn escalation
Context manipulation
Pattern exploitation

TAP (Tree of Attacks with Pruning)

High RiskMulti-turn

Key Features

Tree-based exploration
Intelligent pruning
Iterative refinement

AutoDAN

Very High RiskSingle-turn

Key Features

Genetic optimization
Semantic coherence
Stealth capabilities