How RedShield AI Works
Our platform systematically probes your chatbots, agents, and RAG pipelines for security vulnerabilities. Here's the process from start to finish.
1. Configure your engagement
Tell us what to test and what to look for. You provide the target URL, define sensitive data patterns (API keys, PII formats), out-of-scope topics, and the tools your AI has access to. Choose your attack model and set a rate limit to match your target's capacity.
2. We run the campaign
Our adaptive testing engine starts with a broad set of attack categories and learns as it goes. When it finds a weakness, it generates deeper follow-up probes targeting that specific area. When your system clearly blocks a category, it moves on rather than wasting time. Every response is scored for sensitive data leaks, policy violations, and behavioral anomalies. The result is a thorough, targeted assessment that adapts to your system rather than running the same tests regardless of what it finds.
3. Receive your report
When the campaign completes, you get a professional PDF report with an A through F risk grade, executive summary, detailed findings with exact prompts and responses, remediation guidance, and a full attack log appendix. You can also monitor findings in real time during the campaign.
Attack Library
Our testing engine draws from a continuously growing library of attack categories. These are the starting points for every engagement. During the campaign, the engine generates additional targeted probes based on what it discovers about your specific system.
Single-Turn Probes
Fast, single-prompt attacks that catch common misconfigurations and low-hanging vulnerabilities. These run first to establish a baseline.
- System prompt extraction
- Direct credential probing
- Role-play jailbreaks (DAN, dev mode)
- Instruction override / nullification
- Out-of-scope topic probes
Multi-Turn & Contextual
LLM-crafted attacks that adapt to the target's responses. These exploit conversational context, retrieval systems, and tool access.
- Slow-burn context shifting (5+ turns)
- RAG / knowledge base exploitation
- Tool invocation abuse
- Indirect prompt injection via documents
- Cross-session data probes
Adversarial Edge Cases
Tests for output integrity, fairness, resilience, and reputational risk. These go beyond data leaks to assess the system's overall trustworthiness.
- Hallucination induction
- Authority impersonation
- Compute exhaustion
- Discriminatory output testing
- Brand manipulation