LLM/AI
Penetration Testing
Offensive security assessments for LLMs, AI agents, RAG pipelines, and AI-powered applications. Aligned with the OWASP LLM Top 10 2025.
Starting at $4,500 per engagement
True Adversarial AI Testing
Architecture-aware attacks beyond automated scanning
OWASP LLM Top 10 (2025) Coverage
Complete coverage of the latest LLM-specific vulnerabilities.
LLM01: Prompt Injection
User prompts that alter the LLM's intended behavior — bypassing safety guardrails, extracting sensitive data, or triggering unauthorized actions.
LLM02: Sensitive Information Disclosure
Sensitive information leaking through LLM outputs — exposing PII, credentials, proprietary data, or training data through model responses.
LLM03: Supply Chain
Vulnerabilities in third-party models, training data, plugins, and dependencies that introduce security risks into your AI pipeline.
LLM04: Data and Model Poisoning
Corrupted pre-training, fine-tuning, or embedding data that manipulates model behavior — introducing backdoors or degrading performance.
LLM05: Improper Output Handling
Insufficient validation and sanitization of LLM outputs that can lead to XSS, SSRF, privilege escalation, or remote code execution in downstream systems.
LLM06: Excessive Agency
LLM-based systems granted too much autonomy — executing unintended actions, accessing sensitive systems, or making decisions beyond their intended scope.
LLM07: System Prompt Leakage
Extraction of system prompts that reveal internal logic, security controls, access patterns, or sensitive configuration details.
LLM08: Vector and Embedding Weaknesses
Security risks in vector databases and embedding systems — including poisoned embeddings, retrieval manipulation, and unauthorized access to knowledge bases.
LLM09: Misinformation
LLM-generated false or misleading content that applications rely on for decision-making — hallucinations, fabricated data, and confident but incorrect outputs.
LLM10: Unbounded Consumption
Resource exhaustion through unconstrained LLM usage — denial of wallet attacks, excessive token consumption, and computationally expensive queries.
Attack Techniques
Advanced adversarial techniques for in-depth AI security testing.
Direct & Indirect Prompt Injection
Bypass safety guardrails via user input or poisoned external data sources like documents, web pages, and emails.
Jailbreaking & Guardrail Bypass
Creative attacks that circumvent content policies, safety filters, and alignment through context manipulation.
System Prompt Extraction
Techniques to extract system prompts, revealing internal logic, security controls, and sensitive configuration.
RAG Pipeline Exploitation
Attacks targeting retrieval-augmented generation — poisoning knowledge bases, manipulating retrieval, and exfiltrating data.
Agent & Tool Abuse
Exploit agentic AI workflows to trigger unauthorized tool calls, escalate privileges, or access sensitive systems.
Output Exploitation
Test how downstream systems handle LLM outputs — finding XSS, SSRF, command injection, and other vulnerabilities.
Why AI Security Matters
The model is rarely the vulnerability. The application you built around it is. An assistant wired to a database tool can be talked into running a query nobody intended it to run. A retrieval index that ingests documents your users upload will follow instructions written inside one of them, because to the model there is no difference between your content and theirs. Model output rendered straight into a page becomes cross-site scripting; handed to a shell or an internal API, it becomes command injection. And an endpoint with no consumption limits is a bill somebody else gets to run up.
So we test the pipeline, not the chat box. That means the system prompt and what leaks out of it, the retrieval layer and the embeddings behind it, the tools and actions an agent is permitted to invoke, and every downstream system that consumes model output — mapped to the OWASP LLM Top 10 2025 so findings line up with a framework your team can track. Direct prompt injection is the easy part. The findings that change how a system gets built usually come from indirect injection through content your users control, from agents holding more authority than their task requires, and from output handling written on the assumption that whatever the model returns is safe to use.
Testing is done by an experienced tester who reads how your system is actually wired, not an automated suite firing known jailbreak strings at an endpoint. You get findings with reproduction steps and remediation guidance prioritized by real impact, plus a free retest within 30 days once your changes are in. We work with organizations across the Charlotte, NC area and nationwide.
Prevent Prompt Injection
Identify vulnerabilities that allow attackers to manipulate AI behavior through crafted inputs.
Protect Proprietary Logic
Prevent extraction of valuable model weights, architecture, or training methodologies.
Identify Data Leakage
Find vulnerabilities that expose training data or sensitive information through model outputs.
Free Retesting
Complimentary retest of all findings within 30 days to validate remediation.
Related Services
Explore other security assessments that complement this service.
EU AI Act Cybersecurity Assessment
Article 15 adversarial security testing, AI red-teaming, and Annex IV conformity evidence for high-risk AI systems under Article 15.
Learn moreWeb Application Testing
Thorough manual testing of your web applications against the OWASP Top 10 and beyond.
Learn moreCloud Security Assessment
Configuration review of AWS, Azure, or GCP environments aligned with CIS Benchmarks.
Learn moreFrequently Asked Questions
How much does LLM/AI penetration testing cost?
LLM and AI penetration testing starts at $4,500. Final pricing depends on the number of models, agents, or RAG pipelines in scope, and whether API or source-level access is available. Get a free, scoped quote within 24 hours.
What does an LLM penetration test actually test?
Your application, not the foundation model. We test the system prompt and what can be extracted from it, the retrieval layer and its embeddings, the tools and actions an agent can invoke, and how downstream systems handle model output — all mapped to the OWASP LLM Top 10 2025.
We use a third-party model through an API. Is there anything to test?
Yes, and this is where most real risk lives. You do not control the model’s weights, but you control the system prompt, what data reaches the context window, which tools the model can call, and what your code does with the response. Those are the parts attackers reach, and they are all testable.
Do you test RAG pipelines and AI agents?
Yes. For retrieval-augmented generation we test knowledge base poisoning, retrieval manipulation, and data exfiltration through the index, plus weaknesses in the vector store itself. For agents we test whether tool calls can be triggered without authorization and whether the agent holds more privilege than its task requires.
What access do you need to get started?
API or application access to the system under test, credentials for each user role you want covered, and any documentation of the tools, data sources, and integrations involved. Source-level access is optional and affects scope and pricing. Once that is in place we can typically start within 24 hours.
How is this different from an automated AI security scanner?
Automated tools fire known jailbreak strings at an endpoint and score the responses. They do not know your system prompt, what is in your retrieval corpus, or which tools your agent can call, so they miss indirect prompt injection through user-supplied content, excessive agency in agent workflows, and output-handling bugs in the systems consuming the model’s response.
Is this the same as an EU AI Act assessment?
No. This engagement is offensive security testing of your AI system. The EU AI Act Cybersecurity Assessment covers Article 15 adversarial testing and the Annex IV conformity evidence required for high-risk AI systems. If you need documentation for a regulator rather than findings for your engineers, scope that one instead.
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