LLM penetration testing and AI red teaming
Attack-driven security testing for chatbots, RAG pipelines and AI agents. We test prompt injection, sensitive data leakage, RAG manipulation and unauthorised tool use under controlled conditions, with Turkish and multilingual adversarial datasets. Each finding comes with reproducible evidence, its business impact, a remediation step and a retest criterion.
- Scope
- Chatbots, RAG pipelines, AI agents
- Reporting
- OWASP LLM Top 10, MITRE ATLAS, KVKK
- First step
- 30-minute scoping call
- Confidentiality
- NDA on request
What we test
We analyse the risks to your AI infrastructure in four areas.
Prompt injection and jailbreak
We test whether your models can be manipulated into revealing system instructions, bypassing safety filters or producing harmful content.
- Direct injection
- Jailbreak
- System prompt extraction
- Safety filter evasion
RAG and vector database security
We test context poisoning through document retrieval, unauthorised access to vector databases, and the leakage of personal data under KVKK and GDPR.
- RAG poisoning
- Vector database hijacking
- Personal data masking bypass
- Context poisoning
AI agents and tool misuse
We check whether agents that call tools or external APIs can be coerced into unauthorised actions, such as deleting data, placing spoofed orders or manipulating parameters.
- Tool poisoning
- Spoofed API calls
- Indirect injection
- Privilege escalation
AI red teaming
A multi-vector, scenario-based simulation against your AI pipelines, model endpoints and upstream datasets, from the perspective of a capable threat actor.
- Multi-vector attacks
- Supply chain attacks
- Evasion techniques
- Persistent simulation
For background, read What is AI red teaming?, Enterprise chatbot security testing and OWASP LLM Top 10 2026.
Methodology
An LLM test follows a structured adversarial engineering cycle. It starts with a written scope, and critical findings reach your team as soon as we confirm them.
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Scope and modelling
We identify the target models, system instructions, data sources and API limits.
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Reconnaissance
We map user input points, RAG retrieval flows and the schemas of connected tools.
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Exploitation
We run automated prompt injection, indirect payloads and jailbreak scripts alongside manual techniques.
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Risk assessment
We score findings with CVSS-equivalent metrics and assess their data protection impact.
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Roadmap and briefing
We deliver the final report and defensive prompts, and walk your developers through the findings.
- Average duration
- 5–12 business days
- Proposal
- Within 24 hours of the scoping call
- Retest
- Free for critical and high findings
Duration depends on the complexity of the model and the RAG architecture. For a quick first check against a defined prompt set, we can also run a two-day pilot assessment.
Indirect prompt injection through a RAG document
- Mapping
- OWASP LLM01:2025, ATLAS AML.T0051.001, KVKK Art. 12
- Impact
- A document uploaded to the knowledge base can change the assistant's instructions and make it add customer records to its answers.
- Recommendation
- Separate document content from the instruction context, narrow tool permissions, and filter personal data in the output.
- Retest
- Closed
What you receive
Practical material your engineering team can use to fix the models, and a summary your management can act on.
- Technical LLM report
- Working proof-of-concept prompts, vulnerability descriptions and exact remediation instructions.
- Executive summary
- Business impact, data protection compliance scoring and risk heatmaps for board reporting.
- OWASP and ATLAS mapping
- Every finding classified against the OWASP LLM Top 10 and the MITRE ATLAS matrix.
- Defensive prompt guide
- Prompt templates and guardrails designed for your model endpoints.
- Team briefing
- A technical session with your engineers on how each finding works and how to fix it.
- Retest
- Verification of the fixes you apply. The number of retest cycles depends on the package.
Packages
Packages follow the architectural scale of your AI deployment. The price is set by scope after the scoping call.
Essential
A single chatbot: one model endpoint or chat interface
Priced by scopeSet after the scoping call
- Direct prompt injection testing
- System prompt extraction attempts
- Safety filter bypass checks
- Basic jailbreak scenarios
- Technical report with proof-of-concept prompts
- One retest cycle
Recommended
Professional
A RAG or agent system with a vector database or API tools
Priced by scopeSet after the scoping call
- Indirect prompt injection testing
- RAG context poisoning analysis
- Agent tool use and API permission checks
- Personal data leakage and masking tests
- Custom defensive prompt templates
- Live briefing with your development team
- Two retest cycles
Enterprise
An AI red team programme across multiple models, pipelines and the supply chain
Annual programmeCustom pricing
- Everything in Professional
- Model registry audits (S3, Hugging Face)
- Continuous red team simulations
- Custom MITRE ATLAS threat models
- Data pipeline poisoning checks
- Unlimited retest cycles
Every proposal depends on the scope and on the depth of the target models and systems.
Plan the scope of your LLM penetration test
Tell us about your models, data sources and integrations. You receive a written proposal within 24 hours of the scoping call.