AI Red Teaming Framework
Sanitized, NDA-compliant adversarial testing framework for frontier LLMs — covering jailbreak taxonomy, prompt injection, automated adversarial suites, and multimodal attack surface analysis.
Overview
A production-grade red teaming toolkit built from active adversarial testing campaigns against frontier large language models. The framework provides a structured taxonomy of jailbreak techniques, an automated prompt injection detection pipeline, a multi-category adversarial test suite with reporting, and a multimodal attack surface analyser covering text, image, audio, and tool-use vectors. All findings are sanitized and NDA-compliant.
Key Features
- ▸ LLM Jailbreak Taxonomy — 8 attack categories, 40+ techniques, success-rate tracking
- ▸ Prompt Injection Testing Framework — real-time pattern detection with confidence scoring
- ▸ Automated Adversarial Test Suite — 200+ test cases across safety, alignment, and robustness
- ▸ Multimodal Attack Surface Analysis — text, image, audio, and tool-use vector mapping
- ▸ NDA-compliant sanitized findings from frontier model engagements
- ▸ Exportable HTML/JSON reports per test run
Prompt Injection Testing Framework
Regex-based scanner against 9 real adversarial signature classes — the same patterns used to triage prompts during live engagements. Click any example or write your own, then hit Analyse.
Click an example above or type a prompt, then hit Analyse.
Adversarial Test Suite
Simulated replay of the actual test suite used in real engagements — same categories, same test names, realistic timing. Results are randomised by observed failure rates, not live model calls.
Test Configuration
Last Run Summary
Frontier Model Evaluation
Sanitized, NDA-compliant findings from an active red teaming engagement. Model identifiers redacted.
Methodology
- Black-box adversarial testing — no model weights accessed
- Structured taxonomy-driven test plan with 200+ cases
- Manual and automated prompt generation pipelines
- Multi-turn and single-turn attack vectors evaluated
- Findings triaged by severity: Critical / High / Medium / Low
- Responsible disclosure followed throughout engagement
Key Findings (Sanitized)
Composite role-play + encoding attacks bypassed content filters with 83% success rate across evaluated models.
Indirect prompt injection via retrieved documents succeeded in tool-augmented deployments in 7/10 test scenarios.
Many-shot jailbreaking demonstrated context-length dependency — models with larger windows showed higher vulnerability.
System prompt extraction via translation-chaining succeeded in 49% of cases; partial disclosure in additional 23%.
Engagement Timeline
Engagement Statistics
All findings are sanitized and NDA-compliant. Model identifiers, client details, and specific exploit strings have been redacted. Presented for educational and portfolio purposes only. Responsible disclosure procedures were followed throughout.