LLM Red Teaming · Adversarial AI Security

We break your AI
before attackers do|

Adversarial red teaming for production LLMs.
We find what breaks your system — then document exactly how.

Start an Engagement →See our findings →
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2+
Disclosed CVEs
4
Turns to full prompt extraction
100%
Responsible disclosure

// Disclosed findings

Real vulnerabilities.
Responsibly disclosed.

Every engagement that reaches responsible disclosure is documented and shared with the vendor before any public disclosure.

Security Advisory

Sarvam AI

CRITICAL

Findings

CRITICAL

System prompt extraction

Full confidential instructions recovered in a single session without authentication

CRITICAL

Phishing bypass

Model induced to generate targeted phishing content for Indian users in regional languages

HIGH

Multilingual fraud generation

Harmful content produced across Hindi, Hinglish, and regional language contexts

Disclosure AcceptedRead report →

Security Advisory

KissanAI

CRITICAL

Findings

CRITICAL

Full prompt extraction in 4 turns

Complete system prompt recovered through iterative conversational probing

CRITICAL

Persona override attack

AI identity fully replaced mid-session; safety guardrails bypassed entirely

HIGH

[Language] tag architectural flaw

Architectural routing flaw exposed internal system behavior and configuration

Disclosure SentRead report →

// What we do

Services

LLM Red Teaming

→System prompt extraction
→Jailbreak chaining across model variants
→Persona override & instruction bypass
→Multilingual attack vectors (Hindi, Hinglish, Tamil, Telugu)
→Multi-turn adversarial sequences
→Data leakage via indirect injection

RAG Pipeline Security

→Prompt injection via poisoned retrieval chunks
→Context manipulation & document stuffing
→Source attribution bypass
→Knowledge base exfiltration

AI Agent Security

→Tool misuse & unauthorized action chaining
→Goal hijacking across multi-agent systems
→Memory poisoning in persistent agents
→Privilege escalation via agent instructions

Conversational AI & Chatbots

→Intent bypass & topic restriction evasion
→PII extraction through conversational manipulation
→Brand safety violations
→Guardrail stress testing

AI Model Evaluation

→Safety & alignment auditing
→Robustness under adversarial inputs
→Hallucination & factuality profiling
→Bias detection in model outputs
→Behavioral consistency testing
→Red team-informed evaluation design

Synthetic Data

Custom synthetic data generation for AI training pipelines. Available as a standalone engagement for teams with specific data requirements.

Contact us →

// Engagement process

How it works

01

Scoping Call

We map your model architecture, deployment context, threat model, and attack surface. Agree on scope, timeline, and what a successful engagement looks like.

02

Adversarial Testing

Hands-on red teaming against your live system. Prompt extraction, jailbreak sequences, persona hijacking, multilingual exploits — we document everything we try and what works.

03

Findings Report

A structured report for every vulnerability: severity rating, reproduction steps, attack vector, and concrete remediation recommendations your engineers can act on.

04

Debrief & Support

A walkthrough of all findings with your team. Followed by 30 days of async support for remediation questions, re-testing edge cases, or follow-up analysis.

// Start an engagement

Your AI has
blind spots.

Every production LLM has edges it wasn't designed to defend. Prompt extraction, persona override, indirect injection — we find them before your users do.

support@kalpitlabs.com →