Сайт Redteaming Tool от Raft Digital Solutions

Преимущества

  • What is Red Teaming for GenAI applications?

    Red Teaming for LLM systems involves a thorough security assessment of your applications powered by generative AI.

  • Mitigate Risks

    Avoid costly data breaches, financial losses, and regulatory penalties.

  • Enhance User Trust

    Deliver secure and reliable AI-driven experiences to protect your brand reputation.

  • Comprehensive Security Testing

    Evaluate resilience against both traditional and emerging AI-specific threats.

  • Tool Integration Expertise

    Our experts analyze potential risks arising from plugins, function calls, and interactions with external services.

  • Innovation in Attack Simulation

    We simulate real-world threats, including prompt injection and jailbreak attempts, to test your AI's robustness.

Cases

  • Hack Prompt Injection в RAG-боте

    Simulating an attack to bypass a defense, force a model to disclose classified information, or perform an undesired action

  • Cyberbullying Compliance / Bias / Fairness

    Testing whether there is no systemic discrimination by gender / age / region; checking that answers do not contain stereotypes

  • Report Preparing for audit / checking compliance with regulations

    Collecting data, reports where redtiming tests describe potential risks, obtaining information on how to address them, documenting measures

  • Searching Regular inspections after pre-training of models

    Each release with a model update may reveal new vulnerabilities. Regular redtiming checks make it possible to detect these risks at once and prevent them on production

Red Teaming
Process

Risk Assessment & Threat Modeling

Test Planning & Preparation

HiveTrace Red Scanning

Rescan & Validation

Security Guardrails Implementation

Comprehensive Report Delivery

CI/CD Pipeline Integration

HiveTrace Red: Automated AI Red Team Tool

LLAMATOR report example

Available via pip for easy integration into your workflow

Support for 80+ attacks in Russian and English, automatic translation into other languages possible

Works both with local models and via API

A modular architecture that allows SOTA models to be used to create attacks and evaluate responses

Saving all results and metadata for test reproducibility

Ability to combine attacks to test systems in complex scenarios

Supported by ITMO University &
AI Talent Hub