Find what a VAPT will never find.
AI systems have a fundamentally different attack surface - models, agents, tools, RAG pipelines, memory stores, MCP surfaces. Whatever your AI touches at runtime is in scope for attack and reporting. We test it adversarially, with your architecture and business context built in.
AI systems have an attack surface
that traditional testing doesn't reach.
Prompt injection, broken tenant isolation, cross-user data exposure, persistent AI manipulation - these vulnerabilities exist across every AI deployment, whether you're selling to enterprise accounts, running AI internally, or building on top of third-party models. Standard VAPTs don't find them. Neither do automated scanners.
A traditional penetration test checks authentication, API endpoints, and network configuration. It doesn't test what happens when someone crafts a prompt that extracts your system instructions. It doesn't test whether one tenant can read another's AI-generated artifacts. It doesn't test whether an attacker can plant persistent instructions that survive session logout - or poison a RAG store to execute on every future retrieval. These are the vulnerabilities that create real liability and, where AI processes personal data, direct regulatory exposure.
Everything AI touches at runtime is in scope.
Models, agents, tools, RAG pipelines, memory stores, MCP surfaces, agentic workflows - we build a custom attack model for your specific architecture and test against every known AI attack class. Findings are mapped to OWASP, MITRE ATLAS, and applicable regulations.
Direct and indirect prompt injection, jailbreak variants, delimiter confusion, encoding obfuscation, and context window attacks. Multi-turn sequences that bypass single-turn defenses.
Verbatim and partial extraction of confidential system instructions. The system prompt maps every AI constraint - its exposure makes all subsequent attacks significantly more effective.
Attacker-controlled instructions written to AI memory or preference stores that survive session logout - silently influencing all future interactions for the compromised account.
AI-specific IDOR patterns: cross-tenant artifact access, user ID substitution on AI-generated objects, missing ownership checks on reports, sessions, and profile endpoints.
Injection of malicious documents or instructions into AI retrieval stores. Planted content executes when retrieved - the AI outputs attacker-controlled narratives to every user who triggers that retrieval path.
Direct API manipulation of agent state machines: forcing status transitions that skip human review gates, suppressing approval requirements, self-approving high-risk actions without authorization.
Malicious tool definitions injected via MCP servers that redirect agent actions, exfiltrate parameters, or escalate permissions during multi-step reasoning chains - invisible to the user and to conventional monitoring.
Internal agent name and capability enumeration, sequential ID probing across accounts and user objects, tenant ID leakage via storage naming conventions, and token-flooding for cost abuse.
Access to personal data - profiles, AI-generated briefs, contact intelligence - belonging to other users via predictable identifier substitution. Direct GDPR Art. 32 and DPDPA Sec. 8(7) exposure when confirmed.
AI agents covertly transmitting sensitive context - retrieved documents, user inputs, memory contents - to attacker-controlled external endpoints via tool calls, webhooks, or fabricated hyperlinks embedded in generated output.
Context-first. Business-model-aware. No generic scanner.
We don't run automated scans and call it red-teaming. Every engagement is adversarially modelled against your specific product architecture and ICP threat model.
Threat model scoping
We map your AI architecture: what agents run, what data they access, how tenancy is modelled, what the highest-value attack targets are for a motivated external attacker.
Adversarial execution
We run multi-turn attack scenarios across all nine attack surface categories - with real exploit evidence captured, not scanner output. Every finding is confirmed, not theoretical.
Evidence-grade reporting
Every confirmed finding comes with: attack path description, confirmed evidence, severity mapped to OWASP LLM Top 10 and MITRE ATLAS, real-world precedent, and remediation guidance.
Enterprise-ready deliverable
The assessment report is designed to be shown to enterprise procurement and security teams. It answers their questions before they ask them - and gives you the evidence to close the deal.
Deliverables that answer enterprise security reviews.
Complete findings catalog with confirmed evidence, severity ratings (Critical / High / Medium), OWASP LLM Top 10 mapping, MITRE ATLAS mapping, and remediation roadmap.
Every confirmed finding backed by real attack evidence - not theoretical exposure. Shows enterprise procurement exactly what was exploitable, not just what could be.
Findings mapped to GDPR Art. 25/32/33, EU AI Act Art. 9/14/15, India DPDPA, and US state DPA frameworks where applicable. Designed for compliance-conscious enterprise buyers.
If you sell AI into enterprise accounts,
you need this before
they ask for it.
You're in the middle of a security review and procurement wants evidence you govern your AI. This is that evidence - and it answers questions before they ask them.
You've done the standard VAPT. You know it doesn't cover prompt injection or cross-tenant data isolation. You want to know what an adversary actually finds.
You need to prove your product is safe. Not with a checkbox or a policy doc - with confirmed evidence from an adversarial assessment that can be shown to enterprise buyers.
You're shipping a new AI agent or product to enterprise customers. You want to know what they'll find before they find it - and fix it before it becomes a deal-stopper.
Find the vulnerabilities
before your enterprise customer does.
20 minutes is enough to scope whether your AI product has exposure we can find. Available directly or through AWS Marketplace.