Test your LLM features, agents and voice bots the way an attacker would
Security testing and evidence for SaaS and AI product teams, from a founder who spent eight years building a penetration-testing-as-a-service platform before moving into agentic AI pentesting and LLM red-teaming.
SaaS and AI product companies are shipping copilots, RAG search, autonomous agents and voice bots faster than their security programmes can test them. Traditional pentests cover the web app and API, but rarely the model, its prompts, its retrieval layer or the tools an agent can call. AI helps here in two ways: as the subject of testing, where we probe LLM features for prompt injection, data leakage and excessive agency, and as a testing tool, where agentic test runners explore your app and APIs. It does not replace a human tester's judgement on business logic or severity, and it does not make you compliant on its own. We build tests and evidence your engineers and auditors can verify.
Challenges we hear
New attack surface, old test scope
Pentest scopes still list URLs and APIs, while the new risk sits in system prompts, retrieved documents, tool permissions and model output that flows into other systems.
Non-deterministic behaviour
The same prompt can pass today and fail after a model version change, a prompt edit or a new document in the index, so one-off tests age quickly.
Enterprise buyers asking harder questions
Security questionnaires now ask how you test for prompt injection, RAG tenant isolation and agent permissions, and expect evidence rather than assurances.
Regulation and standards arriving
The EU AI Act, ISO/IEC 42001 and frameworks such as NIST AI RMF mean customers and auditors want documented testing, oversight and risk records.
Where AI fits
AI Security & Compliance
LLM application pentests, agent and voice agent red-teaming, RAG access-control testing, CI regression suites and evidence packs mapped to OWASP, MITRE ATLAS and NIST AI RMF.
Voice AI
Synthetic adversarial callers in English, Hindi and Hinglish that test voice agents across speech recognition, dialogue and tool calls.
Document Intelligence
Questionnaire answering and evidence assembly from your own policies and reports, and embeddable extraction modules for document-heavy SaaS products.
AI Sales & Marketing Automation
Research and personalisation agents that help SDR teams write grounded, cited outreach, and faster security reviews that keep enterprise deals moving.
Use cases
- Featured
LLM Application Penetration Test (OWASP LLM Top 10)
A scoped pentest of your LLM feature and the app around it: prompt injection, data disclosure, output handling, excessive agency and system-prompt leakage, with reproducible evidence.
- Featured
AI Agent Red-Teaming (Tool Misuse and Goal Hijack)
Red-team your AI agents for goal hijacking, tool misuse, excessive agency, privilege abuse and memory poisoning, in a sandbox that records every tool call and decision.
Voice Agent Adversarial Testing
Adversarial test calls to your voice agent in English, Hindi and Hinglish: identity bypass, data disclosure, off-script promises and speech-layer flaws, with PASS, RISK and FAIL evidence.
RAG Data-Leakage and Access-Control Testing
Test whether your RAG pipeline leaks documents across users, roles or tenants through retrieval, caching, citations or logs, using seeded canary documents and role-based test personas.
- Featured
Continuous AI Security Regression in CI
Run versioned prompt-injection, jailbreak and data-leakage suites in CI on every prompt, model, tool or index change, and block releases that regress past agreed severity thresholds.
EU AI Act and ISO/IEC 42001 Evidence Pack
Assemble testing records, risk assessments, oversight design and technical documentation for your AI features, mapped to EU AI Act and ISO/IEC 42001 requirements, ready for counsel and auditors.
Agentic Security Testing for Web Apps and APIs
Supervisor-led AI agents map your web app and APIs, test OWASP Top 10, API and business-logic flaws, confirm each issue live, and hand tester-reviewed findings with evidence to your team.
Security Questionnaire Answering Assistant
Draft answers to security and AI due-diligence questionnaires from your own policies, reports and past answers, with citations, confidence and owner approval before anything goes to a customer.
Document Extraction Module for Your SaaS Product
An embeddable extraction service for your SaaS product: schemas per document type, per-tenant fields, confidence and source boxes, a review component and webhooks, running in your cloud.
Outbound SDR Research and Personalisation Agent
An agent that researches target accounts from public sources, writes cited account briefs and draft first lines for SDRs, and syncs to your CRM, with a person approving every message.
Regulatory context
EU AI Act
Sets risk-based obligations for AI systems placed on the EU market, phased in over several years, including documentation, logging, human oversight, accuracy and cybersecurity expectations for high-risk systems. Whether a given system is in scope is a legal question for your counsel.
ISO/IEC 42001
An international standard for AI management systems. Certification is carried out by accredited bodies; testing records, risk assessments and oversight evidence feed the management system and its audits.
GDPR and India's DPDP Act 2023
Personal data in prompts, logs, embeddings and fine-tuning sets is still personal data, so retention limits, purpose limitation and access rights apply to AI features as they do to the rest of the product.
OWASP Top 10 for LLM Applications, MITRE ATLAS and NIST AI RMF
Voluntary frameworks rather than laws, but widely used by enterprise buyers and auditors as the reference for how LLM and AI risks are identified, tested and managed.
FAQs
01How is an LLM pentest different from a normal web app pentest?
A web app pentest checks authentication, authorisation, injection and configuration in your application and APIs. An LLM pentest adds the model layer: prompt injection, system-prompt leakage, data disclosure through retrieval, unsafe handling of model output and what an agent can do with its tools. We usually test both together, because the most serious issues sit where they meet.
02Will testing affect production?
We prefer a staging environment with production-like configuration and test data. Where production testing is needed, we agree scope, rate limits, test accounts and stop conditions in writing before starting, and avoid destructive actions.
03Can you help us with an ISO/IEC 42001 audit or the EU AI Act?
We help you produce the testing records and technical evidence those processes ask for, mapped to the relevant requirements. Certification decisions belong to your auditor, and legal classification under the EU AI Act belongs to your counsel.
04Do you only test, or do you help fix?
Both. Findings come with root causes and specific fixes, and we can draft remediation changes as pull requests for your engineers to review, then retest to confirm.
Related industries
Fintech & Payments
Clear merchant KYC, dispute and reconciliation queues with AI your risk team can audit
Telecom & Contact Centres
Voice AI, call QA and compliance evidence for Hindi, Hinglish and regional-language calls
Banking
AI for bank operations that cites its evidence, from KYC files to trade documents
Founded by Akhilesh Mittal. 8 years building a security platform for a New Zealand company. IIT ISM Dhanbad and IMT Ghaziabad. Builds production agentic AI.
Scope a first test on one AI feature
Pick the LLM feature, agent or voice bot that worries you most. We will agree a short written scope and show you verified findings with evidence.