AI-Forge: 44 adversarial attacks, 5 regulatory frameworks, live PASS/FAIL per control, audit-grade evidence.
Validation Platform
AI-Forge
Automated AI Compliance Validation Through Live Adversarial Testing
Compliance teams, AI governance leads, security engineers and Authorizing Officials need proof that deployed AI systems behave the way regulators and threat models require. AI-Forge is a prototype that runs real adversarial attacks against real models and produces binary PASS/FAIL findings per regulatory control. Every finding carries the exact prompt sent, the exact response received, a UTC timestamp and a SHA-256 hash. One assessment run produces evidence across five frameworks simultaneously.
Dual-Taxonomy Compliance Matrix
Every attack maps simultaneously to regulatory frameworks (EU AI Act, NIST AI RMF, CSA AI Controls Matrix) and industry threat taxonomies (OWASP LLM Top 10, MITRE ATLAS). One assessment run updates control status across all five taxonomies. A commercial CISO sees OWASP LLM01 and MITRE AML.T0051 in the language their red team speaks. A federal reviewer sees EU AI Act Article 15 and NIST MEASURE-2.7 in the language their Authorizing Official expects. Same data. Both views. Zero duplication.
Audit-Grade Evidence Pipeline
Every finding carries the exact prompt sent to the model, the exact response received, a UTC timestamp and a SHA-256 cryptographic hash of both. Evidence is written to PostgreSQL with full chain of custody from attack execution to control status. Nothing is summarized by AI. Nothing is paraphrased. The evidence record is what actually happened. Verifiable and reproducible.
Attack-First Pacing
Attacks begin firing against the target model within seconds of clicking Run. The regulatory compliance matrix fills in during runtime as findings accumulate. This is not a dashboard that displays pre-computed results. It is a live validation engine that generates evidence in real time while the viewer watches.
Over 30 vendors build AI governance dashboards. Policies, risk registers, documentation, monitoring. None of them run actual adversarial attacks against actual deployed AI models and produce PASS/FAIL findings with cryptographic evidence per regulatory control. That is the gap AI-Forge fills.
What penetration testing gave network security teams, AI-Forge gives AI governance teams. Proof of resilience generated by the same techniques adversaries use, mapped to the frameworks regulators enforce.
How It Works: Prompt Injection Resistance
The operational validator tests LLM resilience against prompt injection, the top attack vector in both federal and commercial AI threat models.
Curated Attack Battery
50 adversarial attacks across four categories:
- Direct injection
- Indirect injection
- Jailbreak and goal hijacking
- Prompt leaking
13 attacks are healthcare-flavored, testing PHI extraction, clinical record injection, care recommendation hijacking and HIPAA-protected disclosure attempts. Every attack traces to a documented 2025 or 2026 incident or an established academic threat pattern. No hypothetical scenarios.
LLM-as-Judge Scoring
A secondary LLM call evaluates whether the primary model complied with the injected instruction. A hybrid override guard prevents the judge from flipping high-precision heuristic compliance markers. The result is a definitive REFUSED, PARTIAL, COMPLIED or LEAKED verdict for every attack, with the judge's reasoning and confidence score recorded as evidence.
Cross-Framework Control Mapping
Each attack carries mappings to EU AI Act articles, NIST AI RMF subcategories, CSA AI Controls Matrix controls, OWASP LLM Top 10 items and MITRE ATLAS techniques. A single assessment run against one model produces findings that update control status across all five taxonomies simultaneously.
Regulatory and Industry Frameworks
AI-Forge validates AI system behavior against published regulatory requirements and industry threat taxonomies.
Regulatory Frameworks
Industry Threat Taxonomies
Anchored to Real Incidents
Every attack in the battery traces to a publicly documented 2025 or 2026 AI security incident.
GTG-1002 (November 2025)
Chinese state-sponsored threat actor used a commercial AI agent to orchestrate autonomous cyber-espionage against approximately 30 organizations. Disrupted by Anthropic. The attack pattern of task decomposition combined with prompt injection is exactly what AI-Forge's jailbreak and goal-hijacking battery tests for.
OpenClaw (January 2026)
Cisco AI Defense documented 9 security findings in an autonomous AI agent skill that performed silent data exfiltration via embedded instructions. AI-Forge's indirect injection battery tests for this exact pattern: adversarial instructions hidden in data the model processes.
LiteLLM / TeamPCP (March 2026)
Supply chain compromise of a widely used AI infrastructure package on PyPI. Three-stage credential stealer with Kubernetes lateral movement and persistent systemd backdoor. Represents the third AI risk surface beyond model manipulation and agent exploitation: infrastructure supply chain integrity.
Assessment Results
Most recent assessment run against Llama 3 8B Instruct via Ollama.
Current Prototype Status
AI-Forge is a functional prototype, not a concept. Here is what has been built and what remains to be validated.
Demonstrated
Requires Validation
Product Family
KSI-Forge validates cloud security controls across NIST 800-53, FedRAMP, CMMC and eight additional risk frameworks. AI-Forge validates AI-specific regulatory controls across the EU AI Act, NIST AI RMF and CSA AI Controls Matrix. Same architectural patterns. Binary PASS/FAIL validators, evidence pipelines, cross-framework mapping. Applied to a different compliance domain. Cybersecurity compliance and AI compliance are related but distinct disciplines, each requiring purpose-built validation.
Read more about KSI-Forge.
AI-Forge validates AI system behavior through automated adversarial testing and produces evidence mapped to published regulatory frameworks. It does not claim regulatory certification, authorization or attestation. Official compliance determinations are made by the relevant governing bodies and their authorized representatives.