AI & LLM Penetration Testing
Large Language Models (LLMs) and AI-integrated applications introduce new, often misunderstood, security risks. At Kairos Sec, we provide expert-level, manual penetration testing tailored to the complexities of AI systems. We uncover the logic flaws, trust boundaries, and misconfigurations that traditional tools and generalized assessments routinely miss.
Whether you’re deploying custom fine-tuned models or integrating third-party LLM APIs into user-facing products, our goal is simple: simulate sophisticated adversaries, expose real risks, and help your team close them with clarity and precision.
Testing Methodology
Our LLM penetration testing follows a structured, model-aware methodology that blends offensive security principles with a deep understanding of machine learning and natural language systems. We treat every engagement as unique—tailored to the architecture, use cases, and threat model of your specific implementation.
1. Reconnaissance & Architectural Review
We begin with a thorough review of your LLM-enabled application, gathering information such as:
- Model provider and configuration (e.g., OpenAI, Claude, Mistral, LLama, etc.)
- Prompt architecture and instruction layering
- External data sources (RAG systems, vector stores)
- Function calling, plugins, or tool usage
- Authentication and user-role structures
- Input channels (web, API, document ingestion, etc.)
This phase ensures that our testing is targeted and fully contextualized within your threat surface.
2. Threat Modeling
Next, we identify and prioritize attack surfaces, focusing on how user input or external data might influence the behavior of the model or surrounding infrastructure. This includes:
- Trust boundaries (e.g., between users and the model, or model and APIs)
- Model-induced control flows (via function calling, workflows)
- Sensitive actions (e.g., file access, data queries, internal service calls)
We apply attacker-centric thinking, mapping out potential abuse paths across the LLM stack.
3. Manual Adversarial Testing
We conduct hands-on, manual testing that simulates how real attackers would interact with and exploit your AI systems. Techniques include:
- Direct and indirect prompt injection
- Jailbreak attempts and system prompt extraction
- Context poisoning via documents, URLs, or user input
- Function misuse and privilege escalation
- Output manipulation, misinformation, and data leakage
- Enumeration of internal services via AI-enabled workflows
All testing is logic-driven, with an emphasis on real-world exploitability and business impact.
4. Risk Analysis & Impact Validation
Findings are validated in context, with a focus on understanding:
- Actual vs. theoretical risk
- User and system roles affected
- Blast radius and potential escalation
- Cross-system implications (e.g., downstream APIs, databases, files)
We provide recommendations that are actionable and matched to your specific architecture and development workflow.
5. Reporting & Retesting
You receive a comprehensive report that includes:
- Executive summary for stakeholders
- Technical breakdown of each issue
- Reproduction steps and proof of concept
- Severity ratings based on real-world impact
- Clear remediation guidance for developers and security engineers
If desired, we also offer retesting to validate fixes and ensure issues are fully resolved.
Why Kairos Sec for AI Security Testing
Full Coverage Across Environments: Hosted and self-hosted models, orchestration layers (LangChain, semantic routers, vector databases), custom plugins, and function-calling ecosystems.
Developer-Friendly Reports: Results are framed for actionable remediation—aligned with both engineering and ML teams.
Manual-First, Context-Aware Testing: We uncover nuanced threats like prompt injection chaining, insecure orchestration, and business logic misuse—missed by tools.
Zero Outsourcing: All testing is performed by senior engineers with deep backgrounds in offensive security and AI/LLM systems.