PLATFORM

Automated AI Red Teaming

[AUTOMATED ADVERSARIAL TESTING]

Continuous, automated red-teaming campaigns that stress-test every agent, model, and workflow before attackers do. If your surface can be exploited, we will find it first.

Execute 60+ adversarial algorithms across multi-turn attack trees, capture policy violations with rich evidence, and map every finding to compliance controls so engineering and security teams can prioritize remediation.

OVERVIEW

Automated AI Red Teaming

Our automated adversarial testing platform (code-named REDit) behaves like a relentless offensive engineer. It bombards your agents and models with jailbreaks, prompt injections, and data-leakage attacks using our proprietary jailbreak agent so you can patch vulnerabilities before shipping.

Proprietary Red-Team Agent

An unsafety-tuned RL model that is rewarded for breaking guardrails, enabling it to reliably coerce target systems into misbehaving where manual testers stall.

Extensive Attack Library

60+ jailbreak algorithms and 100k+ scenario templates spanning multi-turn conversations, obfuscation/encoding tricks, and single-turn exploits deliver breadth and depth no manual exercise can match.

Custom Policy Taxonomy Mapping

Every finding maps to your internal policy codes or industry frameworks like NIST AI RMF, OWASP Top 10, and the EU AI Act so security, compliance, and engineering speak the same language.

Automated & DevOps Ready

Launch attack campaigns through APIs or CI/CD pipelines so every new model, prompt, or release automatically gets battle-tested before reaching production.

Comprehensive Reports & Analytics

Red teaming reports quantify success rates per attack class, provide reproduction steps, and ship full adversarial test suites as evidence for auditors.

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

Connect Your Systems

Import your AI agents and systems through GitHub or API integration for comprehensive testing.

Launch Attack Campaign

Execute diverse adversarial attacks using 60+ algorithms and 100k+ test scenarios to find vulnerabilities.

Review & Remediate

Receive detailed reports with compliance mappings, vulnerability analysis, and actionable recommendations.

ALGORITHMS

Red Teaming Algorithms

Our proprietary red-team agent is trained using unsafety-tuned reinforcement learning, specifically designed to reliably elicit custom policy violations.

With 60+ jailbreak algorithms and 100,000+ adversarial scenarios, our attack library is one of the most comprehensive in the industry.

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?

Convert adversarial research into an always-on service that hardens every release.

Acts as a force multiplier for engineering teams by unleashing automated attackers that stay current with the latest research tricks and prompt abuses.

Exposes vulnerabilities early so fixes land before adversaries or customers discover them, shrinking both risk and remediation cost.

Translates technical findings into compliance artifacts, bridging the gap between red-team activity and governance requirements.

GET STARTED

Ready to stress-test your AI systems?

Join leading organizations using automated red teaming to identify and fix 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, specifically designed to reliably elicit custom policy violations.

With 60+ jailbreak algorithms and 100,000+ adversarial scenarios, our attack library is one of the most comprehensive in the industry.

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