Model & prompt layer
Multi-turn jailbreaks, system-prompt extraction, and indirect injection delivered through documents, images, and retrieved content.
Your AI system has an attack surface no penetration test will reach — and it spans far more than the model. Trampolyne AI runs context-aware adversarial simulations across your model, APIs, tools, and agent workflows, then delivers evidence-grade findings with reproducible proof — not generic scanner output.
Subscription-aware access is loaded automatically.
Jailbreaking the model is table stakes. Real AI breaches happen where the model meets your APIs, your tools, and your business workflow. Trampolyne AI attacks every layer an attacker can reach — and proves the impact with evidence, not theory.
Multi-turn jailbreaks, system-prompt extraction, and indirect injection delivered through documents, images, and retrieved content.
Real authorization testing against your endpoints — including cross-tenant access that names the exact victim record it reached, not a theoretical "could happen".
Exercises your function-calling and Model Context Protocol tools the way an attacker would — including role-forbidden tools and forged tool outputs.
Drives multi-step agent chains into skipping approvals, escalating privilege, or racing state — captured with before/after proof of the change.
And it doesn't end at the report. When you ship a fix, re-run the engagement — every finding comes back with a fixed / still-failing / regressed verdict, so you can prove the gap is actually closed.
Traditional penetration testing was built for code, not conversations. AI systems fail in ways that no port scanner or OWASP ZAP run will ever surface.
Vulnerabilities live in natural-language prompts, system instructions, and multi-turn context — invisible to any tool that doesn't speak to your model the way an attacker would.
The risk isn't a buffer overflow — it's convincing your AI to expose another user's data, bypass an approval workflow, or leak a system prompt. That requires domain-aware attack generation.
A prompt guardrail that held last quarter may break after a model fine-tune or system prompt revision. Point-in-time tests go stale fast. Continuous red teaming catches regressions.
EU AI Act, NIST AI RMF, and sector-specific guidance increasingly require documented adversarial testing of high-risk AI systems. Evidence-grade outputs make compliance defensible.
Each class maps to confirmed MITRE ATLAS tactics or OWASP LLM Top 10 entries and generates targeted, context-aware payloads — not generic templates.
Override system instructions with direct and indirect injection across text, documents, and images.
Elicit the full system prompt through social engineering, jailbreaks, and indirect reasoning chains.
Exploit broken authorization to access another user's data, session context, or conversation history.
Inject adversarial content into the AI's retrieval context to override facts, hijack reasoning, and exfiltrate data.
Manipulate multi-step agentic chains into skipping safety checks, escalating privileges, or invoking unauthorized actions.
Hijack Model Context Protocol tool calls to intercept, redirect, or forge tool outputs in agentic workflows.
Fingerprint the underlying model, version, and provider through probing — surfacing supply-chain and IP exposure risks.
Gradually shift model behavior across a multi-turn session to accept harmful premises or bypass established guardrails.
Trick the AI agent into exfiltrating data through tool calls, summarization chains, or encoded outputs sent to attacker-controlled endpoints.
Point the engine at your AI system endpoint. Describe your org, industry, and data domains. No agents or SDKs to install.
The engine probes your AI system — mapping capabilities, guardrails, tools, and data access before any attack is launched.
Context-aware attack chains run across all selected threat families. Successful attacks are automatically re-run to confirm reproducibility.
Get a structured finding report with full conversation traces, MITRE and OWASP mappings, and severity classification — ready for your security team or board.
Add AI red teaming to your existing security programme. Get findings in the same evidence format your team already works with — no LLM expertise required.
Catch prompt-layer regressions after every model update or system prompt change. Run as part of your CI/CD or pre-release checklist.
Produce documented, repeatable evidence of adversarial AI testing for EU AI Act, NIST AI RMF, SOC 2, and client due diligence requests.
Finance, healthcare, and legal teams deploying AI copilots face heightened data exposure risk. Validate your AI handles sensitive data safely before a breach does it for you.
AI red teaming is adversarial testing specifically designed for LLM-based systems. A regular penetration test looks for code-level vulnerabilities — SQLi, XSS, misconfigurations. AI red teaming looks for behavioral vulnerabilities: can an attacker override your system prompt? Can they access another user's data through the chat interface? Can they manipulate the model into bypassing an approval workflow?
These risks don't appear in CVE databases and can't be detected by any scanner. They require an engine that generates context-aware, business-model-aware attack prompts and evaluates the model's responses the way a skilled human adversary would.
Most "AI red teaming" tools test foundation models in isolation — jailbreaking GPT or testing Claude for harmful content. That's useful, but it's not the risk your business faces.
Trampolyne AI tests your deployed AI application: your system prompt, your RAG pipeline, your tool integrations, your user identity model, your data access patterns. The attack surface is the full stack, not just the model layer. We generate targeted attacks that incorporate your org context, data domains, and known capabilities — the same information an insider threat or determined external attacker would use.
It's fully automated and runs through the dashboard. You configure your target endpoint, describe your organisation and data domains, and select the attack families to test. The engine then runs a four-phase process:
A structured report containing: