amawta
LLM Security

Red teaming and security for LLM systems

We test GenAI applications against attacks, automation failures, and sensitive-information exposure.

LLM system risks appear in prompts, documents, tools, agents, permissions, logs, and human decisions. We evaluate them before they reach operation.

Core tests
01

Prompt injection and instruction abuse

We test whether user input or documents can redirect expected system behavior.

  • Malicious instructions
  • Context hijacking
  • Policy bypass
  • Tool manipulation
02

Information leakage

We review exposure of sensitive data, secrets, internal documents, and conversational memory.

  • Sensitive information disclosure
  • RAG permissions
  • Source filtering
  • Logs and retention
03

RAG poisoning and source quality

We evaluate whether poisoned, outdated, or ambiguous documents affect answers and decisions.

  • Document poisoning
  • Source conflicts
  • False citations
  • Out-of-scope retrieval
04

Excessive autonomy

We test agents, tools, and automation to limit irreversible or unauthorized actions.

  • Excessive agency
  • Tool abuse
  • Insecure output handling
  • Overreliance on outputs
Output

Prioritized findings

Risks ordered by impact, probability, and operational exposure.

Technical reproduction

Prompts, steps, evidence, and conditions to reproduce each failure.

Recommended controls

Guardrails, architecture changes, evaluation, permissions, and human fallback.

Reference source
OWASP Top 10 for LLM Applications

OWASP project for LLM and GenAI application risks.

Is your copilot or agent close to production?

Before scaling it, test how it fails, what data it exposes, and what actions it can take.