AI Red Teaming & LLM Security Testing
Red Asgard is an AI red teaming company that tests LLMs, ML pipelines, AI agents, and AI-powered applications for prompt injection, jailbreaks, data poisoning, and adversarial attacks.
What Is AI Red Teaming?
AI red teaming is the adversarial security testing of large language models (LLMs), machine learning pipelines, and AI-powered applications. As a specialized AI red teaming company, we simulate real attackers to find prompt injection, jailbreaks, data leakage, and adversarial ML weaknesses before they reach production.
Traditional application security testing was not built for non-deterministic AI systems. Our LLM security testing combines manual adversarial testing with automated tooling to probe model behavior, guardrails, retrieval pipelines, and the agents and tools your models can call.
If you ship customer-facing chatbots, copilots, RAG systems, or autonomous AI agents, you need AI security testing. We help product, platform, and security teams manage AI threats across the full model lifecycle.
Comprehensive AI Security Testing
Our AI security testing covers every layer of your machine learning systems, from model behavior and guardrails to AI agents and the AI supply chain.
LLM Security Testing
Comprehensive testing of large language models for prompt injection, data leakage, and unsafe or harmful outputs.
Prompt Injection Testing
Systematic testing of prompt injection vectors across direct, indirect, and multi-turn attack scenarios.
Model Jailbreaking
Adversarial testing to bypass safety guardrails and identify model vulnerabilities and unsafe behavior.
Adversarial ML
Testing ML models against adversarial inputs, data poisoning, and model extraction attacks.
MCP & Agentic AI Security
Security assessments for AI agents, tool use, and Model Context Protocol (MCP) servers, including excessive-agency and tool-abuse attacks.
AI Supply Chain
Review of model provenance, third-party models, plugins, and dependencies for supply-chain and poisoning risks.
What We Test
Aligned to the OWASP Top 10 for LLM Applications and extended for agentic AI and MCP.
Our AI Security Process
A systematic approach to identifying and exploiting AI vulnerabilities.
Threat Modeling
Identify relevant threat actors, attack scenarios, and AI-specific vulnerabilities.
Attack Surface Analysis
Map the complete attack surface of your AI systems, agents, and dependencies.
Adversarial Testing
Execute systematic adversarial attacks, prompt injection, and jailbreak attempts.
Vulnerability Assessment
Analyze findings and assess the real-world impact of identified weaknesses.
Remediation Guidance
Provide actionable remediation recommendations and mitigation strategies.
Monitoring Setup
Establish ongoing monitoring and detection capabilities for future attacks.
AI Security Packages
Choose the AI security package that best fits your testing needs.
LLM Assessment
Comprehensive assessment of large language model security, including input validation and output filtering
MediumComprehensive AI Audit
Full AI security audit including model robustness, data poisoning resistance, and adversarial attack vectors
HighEnterprise AI Security Program
Complete enterprise AI security program with ongoing monitoring and continuous adversarial testing
Very HighAI Security FAQ
Common questions about AI red teaming and LLM security testing.
What is AI red teaming?
AI red teaming is adversarial security testing that simulates real attackers against large language models, ML models, and AI-powered applications to uncover prompt injection, jailbreaks, data leakage, and unsafe behavior before attackers do.
What does LLM security testing cover?
Our LLM security testing covers the OWASP Top 10 for LLM applications: prompt injection, sensitive information disclosure, insecure output handling, excessive agency, model denial of service, and supply-chain risk, across your prompts, guardrails, and retrieval pipelines.
What is prompt injection testing?
Prompt injection testing probes whether untrusted input can override your system instructions or exfiltrate data. We test direct, indirect, and multi-turn injection across chat, RAG, and tool-calling workflows.
Do you test AI agents and MCP servers?
Yes. We assess autonomous agents, tool use, and Model Context Protocol (MCP) servers for excessive agency, tool-abuse, and privilege-escalation attacks that are unique to agentic AI systems.
How much does an AI security assessment cost?
Engagements typically range from $15,000 for a focused LLM assessment to $150,000 for an enterprise AI security program. Final pricing depends on system complexity, scope, and testing depth.
How is AI red teaming different from a penetration test?
A penetration test targets deterministic software and infrastructure. AI red teaming targets probabilistic model behavior, guardrails, and agentic actions, which requires adversarial ML expertise and AI-specific threat models.
AI Security Research
Deep dives from our team on AI red teaming, MCP, and agentic AI security.
Ready to Secure Your AI?
Get expert AI red teaming and LLM security testing to find vulnerabilities in your models, agents, and AI systems before attackers do.