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.
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Built for the two teams that answer for AI risk
Teams building AI products
You ship AI copilots, assistants, and agents to your customers - and a security gap, or an unanswered security questionnaire, can stall a launch or a deal. Trampolyne AI gives you continuous, evidence-grade proof that your AI holds up, so security becomes something you sell with, not a blocker to adoption.
Catch prompt-, API-, tool-, and agent-layer regressions before release
Run pre-release or in CI/CD - no LLM-security expertise required
Answer customer security reviews with mapped, reproducible evidence
Teams securing AI inside the enterprise
Your employees are using internal AI apps that touch sensitive data. You need to know they can't be talked into leaking it across users, exposing a system prompt, or over-reaching their tools - and to show auditors you've tested for exactly that.
Probe internal copilots for cross-user data exposure and prompt leakage
Findings in the evidence format your security programme already uses
Documented proof mapped to the EU AI Act, NIST AI RMF, ISO 42001, GDPR & DPDPA
What you actually get
The three things enterprise buyers ask about
How deeply we test, how defensible the output is, and how an attack engine is kept from being misused.
Pick one to explore - the detail below updates
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.
The four layers we attack
Model & prompt layer
Multi-turn jailbreaks, system-prompt extraction, and indirect injection delivered through documents, images, and retrieved content.
API authorization layer
Real authorization testing against your endpoints - including cross-tenant access that names the exact victim record it reached, not a theoretical "could happen".
Tool & MCP layer
Exercises your function-calling and Model Context Protocol tools the way an attacker would - including role-forbidden tools and forged tool outputs.
Agentic workflow layer
Drives multi-step agent chains into skipping approvals, escalating privilege, or racing state - captured with before/after proof of the change.
Why a VAPT misses it
The surface is a conversation
Vulnerabilities live in prompts, system instructions, and multi-turn context - invisible to any scanner that doesn't talk to your model like an attacker.
Business logic is the bug
Not a buffer overflow - convincing your AI to expose another user's data, skip an approval, or leak a system prompt. That needs domain-aware attacks.
Behaviour drifts every update
A guardrail that held last quarter can break after a fine-tune or prompt change. Point-in-time tests go stale; continuous red teaming catches it.
Regulators now expect it
The EU AI Act, NIST AI RMF and sector guidance increasingly require documented adversarial testing of high-risk AI.
14 attack families - each mapped to OWASP & MITRE
Prompt injection - direct
Direct instruction-override against the system prompt.
OWASP LLM01 · AML.T0051
Prompt injection - indirect
Injected instructions via retrieved content, docs, and images.
OWASP LLM01 · AML.T0051
Data exfiltration
Extracting secrets, system prompts, and PII from the model.
OWASP LLM02 · AML.T0048
Authorization bypass
Role escalation, approval spoofing, and authority abuse.
OWASP LLM06 · Excessive agency
Tool abuse
Unauthorized tool calls and parameter smuggling.
OWASP LLM06 · Tool misuse
RAG attacks
Retrieval steering, context poisoning, and citation forgery.
OWASP LLM08 · AML.T0052
Obfuscation
Encoding, unicode, and multi-step payload hiding to evade filters.
OWASP LLM01 · Evasion
Multimodal injection
Image- and document-borne instruction attacks.
OWASP LLM01 · Multimodal
Agentic planning bypass
Plan and tool-chain manipulation against agentic systems.
OWASP LLM06 · Agentic
Memory poisoning
Long-horizon state corruption and memory steering.
OWASP LLM01 · Multi-turn
Cross-session bleed
Context leakage across users and conversations.
OWASP LLM02 · BOLA
Supply-chain poisoning
Poisoned retrieved content and document supply chain.
OWASP LLM03 / LLM04
Schema confusion
Structured-output and parser-boundary attacks.
OWASP LLM05 · Output handling
API authz - BOLA / BFLA
Object- and function-level authorization probes against your API.
OWASP API01 / API05
It doesn't end at the report. Ship a fix, re-run the engagement, and every finding returns a fixed / still-failing / regressed verdict - so you can prove the gap is actually closed.
We don't just tell you what broke - we tell you which control failed and what it could mean for compliance. Each confirmed finding is mapped to the relevant threat taxonomy and, where applicable, to the specific articles of the regulations governing your AI.
Threat standards
OWASP LLM Top 102025
OWASP API Security Top 102023
OWASP Agentic / GenAIEmerging threats
MITRE ATLASAdversarial ML TTPs
Regulations - mapped article by article
EU GDPRReg. 2016/679
EU AI ActReg. 2024/1689
India DPDPA2023
NIST AI RMFv1.0
ISO/IEC 420012023
OECD AI Principles2024
Cross-tenant data exposure via broken object-level authorizationSample report excerpt
A reproducible conversation trace that names the exact victim record reached - re-runnable on demand, with secrets and PII redacted.
Potential regulatory exposure, surfaced for your legal and compliance review - not a legal determination. Each mapping cites the official article text and is version-tracked, so it holds up under audit.
Handing a team an autonomous attack engine raises an obvious question: what stops it being aimed at the wrong target, or breaking the system it tests? These controls are built in - not optional add-ons.
Authorized targets only
Default-deny by domain: an account can only run against systems it has verified it controls. New targets require explicit authorization - so the engine can never be pointed at someone else's AI.
Production-safe by default
Never issues destructive requests (no PUT / PATCH / DELETE) and skips destructive tool and MCP actions in production. Declarative rules-of-engagement are enforced at a single chokepoint across every executor.
SSRF-guarded & scope-confined
Refuses to touch internal, loopback, reserved, or cloud-metadata addresses - always on, regardless of configuration. Every run stays confined to the endpoint you provide.
Evidence without exposure
Secrets and PII are redacted before anything is written to storage; raw sensitive values are never persisted. Attacker payloads and canaries stay intact, so findings remain fully reproducible.
Data residency & encryption
Hosted on AWS in ap-south-1 (Mumbai); AES-256 at rest and TLS 1.2+ in transit. Security-relevant events are written to a tamper-protected audit log with 365-day retention.
Scoped, abortable, repeatable
Every engagement is cooperatively abortable mid-run and re-runnable after a fix, returning a fixed / still-failing / regressed verdict per finding - so you can prove the gap is actually closed.
Frequently Asked Questions
What is AI red teaming, and why is it different from a regular pentest?
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.
How is this different from model-only red teaming tools?
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.
What does the engagement process look like?
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:
Recon - probes your AI to map capabilities and guardrails
Exploit - runs multi-turn attack chains across all selected threat families
Verify - re-runs successful attacks to confirm reproducibility
Judge - each finding scored for severity, with evidence verified against the actual transcript (no hallucinated quotes)
What do I get at the end of a run?
A structured report containing:
Full finding report - every confirmed vulnerability with description, severity, and remediation guidance
Confirmed evidence - the exact conversation traces that produced each finding, reproducible on demand
Standards & regulatory mapping - each finding mapped to the OWASP LLM / API / Agentic Top 10 and MITRE ATLAS, plus article-level exposure under GDPR, the EU AI Act, India's DPDPA, NIST AI RMF, ISO/IEC 42001, and OECD AI Principles - with version-tracked citations
Regression re-testing - re-run after a fix and every finding returns a fixed / still-failing / regressed verdict
Is it safe to point this at our production system?
Yes - restraint is built in, not bolted on. The engine runs only against targets you have verified you control: access is default-deny by domain, and a new target must be explicitly authorized before any run can start, so it can never be aimed at someone else's AI.
In production it is non-destructive by design - it never issues PUT / PATCH / DELETE requests or destructive tool/MCP actions, and declarative rules-of-engagement are enforced uniformly across every executor. An always-on SSRF guard refuses internal, loopback, and cloud-metadata addresses, and each run stays confined to the endpoint you provide. Secrets and PII are redacted before anything is stored. Many customers still prefer to start against a staging environment - that works too.
Do I need to install anything or expose my system?
No agents, SDKs, or code changes are required. The engine communicates with your AI system over HTTPS exactly as a real user would. You only need to provide the endpoint URL and an API key or bearer token. Your system does not need to be publicly accessible - you can use a staging environment or allow-list the engine's IP range.
How long does a run take?
Runtime depends on the number of attack families selected, the recon depth, and your AI system's response latency. A focused single-family run typically completes in 15–30 minutes. A full-spectrum run across all 14 attack families usually takes 1–3 hours. Results are available in the dashboard as soon as each phase completes.
Can I start in demo mode first?
Yes. You can explore the full dashboard configuration interface in demo mode without a subscription. To execute a live run against your AI system and access reports, subscribe through AWS Marketplace. Subscriptions are usage-based with no long-term commitment.
Trampolyne AI
Account access
Trampolyne AI — ARTS
Data Handling & Privacy Practices
What data we collect
When you create an account or use the service, we collect:
Identity data — email address, display name, and hashed password (bcrypt; never stored in plaintext)
AWS identifiers — AWS Marketplace Customer ID and subscription token, used solely for billing and entitlement verification
Run configuration data — target endpoint URLs, system-prompt descriptions, and attack-family selections you provide when setting up a red-team run
Attack & result data — generated attack prompts, model responses captured during testing, scoring verdicts, and evidence snippets included in reports
Session & quota metadata — session tokens (httpOnly cookies), run counts, entitlement records, and timestamps
How we store and protect it
All data is stored in Amazon Aurora PostgreSQL running in AWS region ap-south-1 (Mumbai), with AES-256 encryption at rest and TLS 1.2+ in transit
Database credentials are managed exclusively through AWS Secrets Manager and are never embedded in application code
The application runs on AWS App Runner in ap-south-1 (Mumbai). Persistent storage stays in this region; the only data processed outside it is the transient AI-model processing described under Data sharing below
Passwords are one-way hashed with bcrypt before storage and are never recoverable or transmitted in readable form
All security-relevant events (sign-ups, logins, subscription changes, run completions) are written to a tamper-protected Amazon CloudWatch Logs group with a mandatory 365-day retention policy
How we use your data
Your data is used exclusively to:
Deliver and operate the AI red-teaming service you subscribed to
Display run results and historical reports in your account dashboard
Track and enforce subscription quotas and entitlements through AWS Marketplace Metering Service
Maintain security audit logs as required by AWS Marketplace compliance policy
Data sharing
We share data with no third parties for advertising, analytics, or resale. The only external sharing is:
AI model providers — to generate adversarial test cases and evaluate responses, run content (your configuration and the target system's replies) is processed by third-party large-language-model providers. This processing is transient and solely to return the result; providers are contractually restricted from using your content to train their models
AWS Marketplace Metering Service — run counts (integers only) are reported for billing purposes, as required by the AWS Marketplace Seller Agreement
Email delivery — your email address and name are passed to our transactional email provider solely to send account confirmation messages. No marketing emails are sent without explicit opt-in
You may request a copy of the data we hold about you, correction of inaccurate data, or deletion of your account at any time by emailing contact@trampolyneai.com. We will respond within 30 days.
Security incidents
In the event of a breach or unauthorized access affecting your data, we will notify affected users at the email address on file within 72 hours of confirmed discovery. Report security concerns to contact@trampolyneai.com.
Changes to this policy
Material changes will be communicated by email to registered users at least 14 days before taking effect. Continued use of the service after the effective date constitutes acceptance.