---
title: Automated RedTeaming for GenAI
slug: automated-redteaming-for-genai
docTags: 
createdAt: 2025-03-05T03:04:51.955Z
---

# Automated RedTeaming for LLMs

## Overview

AISpectra's Automated RedTeaming for GenAI performs assessments to evaluate safety, security and privacy for models deployed in diverse **cloud environments** and **LLM Frameworks**.

:::ExpandableHeading
## User Manual

For complete usage instructions, please refer to the [LLMRT user manual](https://aisdocs.blob.core.windows.net/reference/LLM_RT/AISpectraV0425_LLMRT_User_Manual.pdf) 
:::

The following categories are considered during the assessment:

***

## 1. Safety

### Toxicity

Evaluate whether the model generates toxic, offensive, or harmful content. This includes hate speech, threats, abusive language, and any harmful biases.

### Not-Safe-For-Work (NSFW)

Assess the generation of content that is sexually explicit, graphic, or otherwise inappropriate for professional environments.

### Generic Harm

Review content that could cause harm, either directly or indirectly, through misinformation, self-harm encouragement, violence incitement, or dangerous instructions.

***

## 2. Security

### Jailbreak

Test for potential methods to bypass guardrails, enabling the model to output restricted or harmful content. This includes prompt injection and adversarial prompt testing.

### Sensitive Information Disclosure

Assess if the model can leak confidential, internal, or sensitive information.

***

## 3. Privacy

### PII (Personally Identifiable Information)

Test for accidental or intentional generation of personal data, such as names, addresses, phone numbers, or any information that can identify individuals.

### Sensitive PII

Evaluate for disclosure of sensitive personal data, including medical records, government-issued identifiers (e.g., Social Security Numbers), and other high-risk personal data.

***

## Supported Cloud Environments

- Azure OpenAI
- AWS Bedrock
- Google Cloud Platform
- Databricks LLM Deployment
- On-Prem / Self-hosted LLMs (Example: Models from Hugging Face)

***

## Supported Task Types

- Chatbot
- Instruction
- Question and Answering
- Summarization
- Visual (For multi-modal models)

***

## Reporting

1. **JSON Report**
   -  Machine-readable format optimized for easy ingestion into MLOps pipelines.
   - Contains: Risk classifications, Attack Success Rate / Rejection Rate, Example Prompts and Metadata (model version, environment, timestamp) 
2. **PDF Report**
   - Executive summary for enterprise and leadership teams. 
   - Includes: Overall risk posture, Category-wise risk breakdown, Key observations & insights, Recommendations for remediation 
3. **Dashboard **
   - Interface providing: Consolidated view of all LLM assessments, Per-model risk overview, Category-wise details (Safety, Security, Privacy) 
   - Compliance and Standards coverage details - OWASP Top 10 for Generative AI/LLM, EU AI Act, MITRE ATLAS, NIST AI Risk Management Framework, ISO/IEC 42001

***