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Trampolyne AI Red Teaming Platform

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Trampolyne AI runs automated, multi-turn, multi-modal adversarial testing against your live AI systems — chatbots, copilots, RAG apps, and agents — using attack scenarios generated from your actual business context, identities, and data domains, not generic templates. Unlike traditional VAPT, which can't reach AI-specific attack surfaces, Trampolyne tests every layer an attacker can — not just the model. Beyond prompt injection, jailbreaks, and system-prompt extraction, it directly exercises the API authorization layer (BOLA/IDOR with a cross-tenant differential that names the exact record it reached), tool and Model Context Protocol (MCP) calls, and multi-step agent workflows (approval bypass, privilege escalation, data exfiltration). Runs are configurable by attacker access level, org context, and attack family, on-demand or scheduled continuously — with regression re-testing that returns a fixed / still-failing / regressed verdict per finding. Output is a severity-ranked, evidence-grade report: every confirmed finding ships with a reproducible conversation/request trace and is mapped to the OWASP LLM, API, and Agentic Top 10 and MITRE ATLAS — plus article-level regulatory exposure under GDPR, the EU AI Act, India's DPDPA, NIST AI RMF, ISO/IEC 42001, and OECD AI Principles. Formatted for enterprise security review and procurement questionnaires. Safe by design: Trampolyne runs only against targets you've verified you control, stays non-destructive in production, is SSRF-guarded, and redacts secrets/PII before storage. Used by AI-native SaaS companies to pass enterprise security reviews and by security teams to continuously validate AI in production — with findings translating directly into Trampolyne's governance policies, closing the loop between testing and enforcement.

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