What is AI Red Teaming?
AI red teaming is adversarial testing of LLM-based applications and the agents built on top of them. We treat your model, its system prompt, its retrieval data, and every tool it can call as one attack surface and try to make it do what it should refuse: leak another user's data, run an action it was never authorized to run, or follow instructions that arrived hidden inside a document.
A standard penetration test checks the web app around the model. It rarely touches the model's own failure modes. Prompt injection does not show up in a SAST scan. A jailbreak that turns your support bot into an open data tap does not register as a CVE. These are behavioral flaws, and the only way to find them is to attack the behavior.
We test against the OWASP Top 10 for LLM Applications and extend it for agentic systems: tool-calling, RAG poisoning, and excessive agency. You get a report that names each successful attack, the exact prompt that triggered it, the data or action it exposed, and the control that stops it.
What This Catches
- Direct and indirect prompt injection, including payloads hidden in retrieved documents
- Jailbreaks that bypass safety instructions and content policy
- Training-data and system-prompt leakage through the chat interface
- Cross-user data exposure in multi-tenant RAG pipelines
- Tool and function-call abuse: actions the agent should never take
- Insecure output handling that turns model text into XSS or SQL injection
AI Red Teaming vs. Standard Penetration Testing
Both matter. They find different classes of flaw, and one will not catch the other.
AI Red Teaming
- Attacks model behavior: injection, jailbreaks, leakage
- Tests the system prompt, RAG data, and tool calls
- Finds cross-user leakage in multi-tenant pipelines
- Probes agent autonomy and excessive agency
- Maps to the OWASP Top 10 for LLM Applications
- Output is behavioral: the prompt, the response, the fix
Standard Penetration Testing
- Attacks the app and infrastructure around the model
- Tests auth, access control, network, and config
- Finds known vulnerability classes and CVEs
- Scope is the code and the stack, not the model
- Maps to the OWASP Top 10 for web or mobile
- Output is technical: the endpoint, the exploit, the patch
When You Need AI Red Teaming
If you have shipped or are about to ship an LLM feature that touches customer data, takes actions, or speaks for your brand, the model is in production and so is its attack surface. A pentest of the surrounding app will not tell you whether a support ticket can hijack your agent.
Run AI red teaming before launch on anything customer-facing, and again after any change to the system prompt, the retrieval source, or the set of tools the agent can call.
What We Test
Engagements scoped to how your AI is actually built and what it can reach
LLM Application Assessment
Full adversarial test of a chatbot or LLM feature: prompt injection, jailbreaks, system-prompt extraction, and insecure output handling against the live application.
AI Agent & Tool-Use Testing
We target agents that call tools, APIs, and code. The goal is to make the agent take an unauthorized action or chain a tool call into real impact.
RAG Pipeline Security
Testing retrieval-augmented generation for indirect injection through ingested documents, cross-tenant data bleed, and poisoning of the knowledge base.
Guardrail & Filter Bypass
Adversarial evaluation of your safety layer: content filters, input sanitizers, and output validators, to see what gets through and how reliably.
System Prompt & Data Leakage
Extraction attacks against your system prompt, hidden instructions, and any training or context data the model can be coaxed into revealing.
Continuous AI Red Teaming
A recurring program for teams shipping AI fast: re-testing on every prompt, model, or tool change so a regression does not reach production silently.
How We Run an Engagement
A structured attack, not a one-off prompt-poking session
Scope & Threat Model
We map what the AI can read, say, and do: its data sources, its tools, its users, and the worst outcome for each. That defines the objectives we attack toward.
Recon & Behavior Mapping
We learn how the model responds, where the guardrails sit, and what the system prompt likely contains, before we try to break any of it.
Direct Prompt Attacks
Jailbreaks, role-play bypasses, encoding tricks, and instruction overrides aimed straight at the chat interface to defeat the safety layer.
Indirect Injection
We plant payloads in the places the model reads from: documents, web pages, RAG sources, and user fields, then watch the model execute them.
Tool & Agency Abuse
For agents, we push for unauthorized tool calls, privilege misuse, and chained actions that turn a benign request into real-world impact.
Data Exfiltration
We test whether the model leaks its system prompt, another user's data, or context it should never surface, and how far that leak reaches.
Impact Analysis & Reporting
Every successful attack is documented with the exact prompt, the response, the data or action exposed, and a severity rating you can act on.
Remediation & Retest
We hand your team the controls that close each finding, then retest after the fix to confirm the attack no longer works.
Mapped to the OWASP Top 10 for LLM Applications
Every finding maps to the OWASP Top 10 for LLM Applications, the reference list maintained by the OWASP GenAI Security Project. It gives your team a shared vocabulary for what failed and why.
The report ties each successful attack to its OWASP LLM ID, so your developers and any auditor can see the risk class, the proof, and the fix in one place.
Prompt Injection
Crafted input overrides the model's instructions.
Sensitive Information Disclosure
The model leaks data it should withhold.
Supply Chain
Compromised models, datasets, or plugins.
Data & Model Poisoning
Tainted training or retrieval data skews output.
Improper Output Handling
Model text is trusted into XSS or SQL injection.
Excessive Agency
The agent has more permission than it should.
System Prompt Leakage
Hidden instructions get extracted by the user.
Vector & Embedding Weaknesses
Flaws in the RAG retrieval and embedding layer.
Misinformation
Confident, wrong output that users act on.
Unbounded Consumption
Resource abuse driving denial of service or cost.
Why Reconix for AI Red Teaming?
Offensive Background, Applied to AI
We are penetration testers first. We bring an attacker's instinct to LLM systems instead of running a checklist of canned prompts.
Full-Stack, Not Just the Prompt
We test the model, the RAG pipeline, the tools it calls, and the app around it. A leak usually lives at the seam between two of those.
OWASP LLM Aligned
Findings map to the OWASP Top 10 for LLM Applications, so the output is auditable and your team knows exactly which risk class each issue belongs to.
Reproducible Proof
Every finding ships with the exact prompt and response that triggered it. Your developers can reproduce the attack and confirm the fix.
Built for Teams Shipping Fast
AI features change weekly. We structure engagements so you can retest after a prompt, model, or tool change without starting over.
Regulatory Alignment
Compliance Requirements This Service Supports
Our testing methodology is designed to meet the requirements of Thailand's key cybersecurity regulations.
Frequently Asked Questions
Common questions about AI red teaming for LLM applications and agents.
Ship Your AI Knowing It Was Attacked First
Before your chatbot or agent reaches customers, let Reconix attack it the way an adversary will. You get the working exploits and the controls that stop them.
Reconix is the proof-first offensive security specialist in Thailand, serving regulated digital businesses of all sizes.
Related Services
Web Application Penetration Testing
Test the application that wraps your AI: auth, access control, and the API layer.
Red Teaming & Adversary Simulation
Goal-based simulation of a real attacker across your people, process, and technology.
Secure Code Review
Source-level analysis of the code that builds prompts, calls the model, and handles its output.