AI Red Team Engagement
Niriksha|
We attack your AI product the way a real adversary would. Prompt injection, jailbreaks, RAG poisoning, agent misuse, multilingual exploits, and output leakage are tested against the system your users actually touch.
What we test
We look for AI-specific failures, not just web bugs.
Niriksha is for teams shipping LLM features where normal security testing does not cover the model behavior, context boundaries, tool permissions, or language attack surface.
Prompt injection
Direct and indirect instruction override attempts against chat, RAG, agents, and tool-using workflows.
Jailbreak chains
Multi-step attempts to weaken policy boundaries, shift personas, or create unsafe compliance over time.
Data extraction
Attempts to reveal system prompts, hidden context, customer data, internal URLs, credentials, or private documents.
Multilingual abuse
Hindi, Tamil, Urdu, Hinglish, transliteration, and mixed-script payloads that English-only testing often misses.
Agent misuse
Tool-call abuse, unsafe workflow execution, document-borne instructions, and permission boundary failures.
Output risk
PII leakage, unsafe recommendations, regulated-domain claims, hallucinated policy, and brand-risk responses.
Engagement process
From scope to remediation.
Scope
We map the product, user roles, model providers, tools, retrieval sources, allowed behavior, and business-critical failure modes.
Attack
We run manual adversarial testing across single-turn, multi-turn, multilingual, RAG, agent, and output-risk scenarios.
Document
Every confirmed issue is written with severity, impact, reproduction steps, evidence, and remediation guidance.
Debrief
We walk your team through the findings, answer engineering questions, and support remediation follow-up.
What you receive
Findings your engineers can reproduce and fix.
The output is not a generic PDF. Each issue is tied to observed behavior, real payloads, business impact, and concrete remediation options.
Ready to test your AI system?
Start with a scoped Niriksha engagement.
Best fit for production LLM apps, customer-facing copilots, internal agents, RAG workflows, and regulated-domain assistants.