AI Security Testing and LLM Red Teaming for the Age of Autonomous AI
Your AI chatbot handles customer data. Your AI agent executes code. Your RAG pipeline retrieves from your knowledge base. One prompt injection can turn your AI into an adversary's tool. CyberGuards' AI security specialists in San Francisco test the vulnerabilities that traditional security teams are not equipped to find.
AI Introduces an Entirely New Attack Surface
AI Systems Are Not Just Software
Traditional security testing assesses deterministic software. AI systems are probabilistic — they generate different outputs for similar inputs, make autonomous decisions, and can be manipulated through natural language. This fundamental difference means conventional penetration testing methodologies miss entire classes of AI-specific vulnerabilities like prompt injection, jailbreaking, and model manipulation.
The Stakes Are Rising Fast
Organizations across San Francisco and the Bay Area are deploying AI agents with access to internal tools, customer databases, and business-critical systems. A prompt injection attack against an AI agent with database access is not a theoretical risk — it is an SQL injection delivered through natural language. The EU AI Act, NIST AI RMF, and emerging state regulations are mandating AI risk assessments.
Complete OWASP LLM Top 10 2025 Coverage
Every AI security assessment includes systematic testing against the OWASP Top 10 for LLM Applications 2025 — the emerging standard for AI application security.
Prompt Injection
Direct and indirect prompt injection attacks that manipulate the LLM to bypass instructions, override system prompts, exfiltrate data, or execute unauthorized actions. We test with adversarial prompts, encoded payloads, multi-turn manipulation, and context-window poisoning techniques.
Sensitive Information Disclosure
Testing for leakage of training data, system prompts, API keys, PII, and confidential business information through model outputs. We probe for memorization artifacts, data extraction through crafted queries, and metadata exposure through error messages and model behavior.
Supply Chain Vulnerabilities
Assessment of risks from third-party model providers, pre-trained model integrity, fine-tuning data poisoning, plugin and tool vulnerabilities, and dependency risks in the AI/ML supply chain including compromised model weights and backdoored training pipelines.
Data and Model Poisoning
Evaluation of your training and fine-tuning data integrity, RAG knowledge base contamination risks, and adversarial input resistance. We assess whether attackers can influence model behavior by poisoning the data your AI system learns from or retrieves.
Improper Output Handling
Testing how your application processes and renders LLM outputs. If model responses are passed to downstream systems without sanitization, prompt injection can chain into XSS, SQL injection, command injection, or SSRF through the model's generated output.
Excessive Agency
Assessment of AI agent permissions, tool access scope, and autonomy boundaries. We test whether AI agents can be manipulated to execute actions beyond their intended scope — accessing files, making API calls, modifying data, or performing operations the user should not be able to trigger through the AI.
System Prompt Leakage
Extraction of system prompts, internal instructions, and configuration details that reveal your AI system's guardrails, business logic, and security controls. Leaked system prompts give attackers a roadmap to bypass your defenses.
Vector and Embedding Weaknesses
Testing vector database access controls, embedding poisoning, semantic search manipulation, and retrieval augmentation exploits in RAG pipelines. We assess whether attackers can influence what your AI retrieves and references by manipulating the vector store.
Misinformation
Assessment of hallucination risks, factual accuracy controls, and the potential for adversaries to use your AI system to generate convincing but false information. We test whether your guardrails prevent your AI from becoming a misinformation vector.
Unbounded Consumption
Testing for denial-of-service through expensive queries, token exhaustion, recursive tool calling, infinite loops in agent workflows, and resource abuse that could result in significant cost overruns or service degradation for your AI infrastructure.
AI-Powered Testing with Human-in-the-Loop Expertise
We combine automated AI red teaming tools with experienced human operators to deliver testing that is both comprehensive and creative.
Automated AI Red Teaming
Our automated testing harness generates thousands of adversarial prompts, fuzzes input boundaries, and systematically tests guardrail bypasses at scale. Automated testing provides breadth — covering known attack patterns, encoding variations, and jailbreak templates across your entire AI surface area.
Human-in-the-Loop Adversarial Testing
Experienced security engineers craft custom attack strategies based on your specific AI system, its tools, its data sources, and its business context. Human testers discover novel bypass techniques, chain vulnerabilities across the AI and traditional application stack, and test complex multi-turn social engineering scenarios that automated tools cannot replicate.
AI Systems We Test
LLMs & Chatbots
Customer-facing chatbots, internal AI assistants, and LLM-powered features built on GPT, Claude, Llama, Mistral, or custom fine-tuned models. We test prompt injection, jailbreaking, data leakage, and guardrail bypass.
AI Agents
Autonomous AI agents with tool use, function calling, and multi-step reasoning capabilities. We test agency boundaries, tool access controls, action authorization, and whether agents can be manipulated to perform unintended operations.
RAG Pipelines
Retrieval-Augmented Generation systems that ground LLM responses in your knowledge base. We test retrieval poisoning, document injection, context window manipulation, and data exfiltration through the retrieval mechanism.
ML Endpoints
Machine learning model inference APIs, prediction endpoints, and model serving infrastructure. We test adversarial inputs, model evasion, input validation, and the API security of your ML serving layer.
Vector Databases
Pinecone, Weaviate, Qdrant, Chroma, and other vector stores. We test access controls, embedding manipulation, similarity search abuse, and the security of your vector database infrastructure and APIs.
AI-Integrated Applications
Traditional web and mobile applications with AI features — AI-generated content, AI-powered search, AI recommendations, and AI-assisted workflows. We test the integration points where AI meets your application security boundary.
AI Security Framework Alignment
Our assessments map to the emerging frameworks governing AI security and risk management.
NIST AI RMF
The NIST AI Risk Management Framework provides our foundation for AI risk assessment. We map findings to the Govern, Map, Measure, and Manage functions, addressing AI trustworthiness characteristics including security, resilience, and privacy.
MITRE ATLAS
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) provides the adversarial technique taxonomy for our AI testing. We map every attack technique to the ATLAS matrix, providing your team with standardized threat intelligence for AI-specific attacks.
EU AI Act
For organizations subject to the EU AI Act, our assessments address high-risk AI system requirements including risk management (Article 9), data governance (Article 10), technical documentation (Article 11), and human oversight (Article 14) provisions.
What You Receive
AI Security Report
Comprehensive report covering AI-specific and traditional application findings, with OWASP LLM Top 10 mapping, MITRE ATLAS technique references, proof-of-concept adversarial prompts, and prioritized remediation guidance for your AI and engineering teams.
Attack Playbook
Documented adversarial techniques, bypass strategies, and attack chains specific to your AI system. Your security team can use this playbook to build internal AI red teaming capabilities and regression test future AI deployments.
Guardrail Recommendations
Specific, actionable recommendations for improving your AI guardrails, content filters, system prompt hardening, output sanitization, and agent permission boundaries based on our testing results.
Organizations That Need AI Security Testing
AI-Native Companies
San Francisco and Bay Area AI companies building LLM-powered products need to validate the security of their AI systems before customers trust them with sensitive data and business-critical workflows.
Enterprises Deploying AI
Large organizations integrating AI into customer service, internal operations, and product features need assurance that their AI deployments cannot be weaponized against them through prompt injection and jailbreaking.
Regulated Industries
Healthcare, financial services, and government organizations deploying AI must demonstrate AI risk management aligned with NIST AI RMF, EU AI Act, and industry-specific requirements.
AI Platform Providers
Companies providing AI infrastructure, APIs, and platforms to other businesses need third-party validation that their AI services are resilient against adversarial attacks and safe for customer use.
AI Security Testing FAQ
What is AI security testing?
AI security testing is a specialized assessment of artificial intelligence systems — including LLMs, chatbots, AI agents, RAG pipelines, and ML models — for security vulnerabilities unique to AI. This includes prompt injection, jailbreaking, data poisoning, model theft, and the full OWASP Top 10 for LLM Applications. Unlike traditional penetration testing, AI security testing requires understanding both cybersecurity and machine learning attack surfaces.
What is LLM red teaming?
LLM red teaming involves adversarial testing of large language model applications to identify ways an attacker can manipulate, bypass, or abuse the AI system. Our red team operators use prompt injection, jailbreak techniques, context manipulation, and multi-turn attack strategies to test your AI guardrails, content filters, and safety mechanisms under realistic adversarial conditions.
What types of AI systems do you test?
We test large language models (GPT, Claude, Llama, Mistral, and custom fine-tuned models), AI-powered chatbots and virtual assistants, autonomous AI agents with tool use, RAG (Retrieval-Augmented Generation) pipelines, ML inference endpoints, vector databases, embedding APIs, and AI-integrated applications. Our testing covers both the AI components and the traditional application security of the surrounding system.
What is the OWASP Top 10 for LLM Applications?
The OWASP Top 10 for LLM Applications (2025 edition) is the definitive list of the most critical security risks for applications that integrate large language models. It covers prompt injection, sensitive information disclosure, supply chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.
Can you test AI systems built on third-party models like OpenAI or Anthropic?
Yes. Most AI security vulnerabilities exist in the application layer — how your system uses the model, not in the model itself. We test your system prompts, guardrails, function calling implementations, RAG retrieval logic, input/output filtering, and the integration layer between your application and the third-party model API. We focus on what you control and can fix.
How long does an AI security assessment take?
Duration varies with system complexity. A focused chatbot assessment typically takes 1 to 2 weeks. A comprehensive AI agent with tool use, RAG pipeline, and multiple model integrations may require 3 to 4 weeks. Continuous AI red teaming retainers are also available for organizations that deploy AI systems iteratively. We scope the timeline after understanding your specific AI architecture.
Do you provide guidance on AI governance and compliance?
Our assessment reports include mapping to relevant AI governance frameworks: NIST AI Risk Management Framework (AI RMF), MITRE ATLAS, and the EU AI Act risk categories. We provide actionable recommendations for implementing AI-specific security controls, monitoring, and governance processes that align with emerging regulatory requirements.
What makes your AI security testing different from automated AI safety tools?
Automated red teaming tools test predefined prompt patterns. Our human-in-the-loop approach combines automated fuzzing with creative adversarial thinking from experienced security engineers. We develop custom attack strategies based on your specific AI system, discover novel bypass techniques, and test complex multi-turn attack scenarios that automated tools cannot replicate.
Ready to Secure Your AI Systems?
Our San Francisco AI security team will test your LLMs, chatbots, and AI agents against the OWASP LLM Top 10. Get a free scoping call.
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