AI Security
As you ship AI features, chatbots and agents, you expose a brand-new attack surface. Attack Insights penetration tests your AI applications against the OWASP Top 10 for LLMs — and proves what an attacker could actually do.
Definition
LLM-powered apps — assistants, copilots, RAG pipelines and autonomous agents — introduce risks that traditional application security was never designed to catch: prompt injection, data leakage through model responses, over-privileged agents, leaked system prompts and poisoned data or models. The OWASP Top 10 for LLM Applications is the industry reference for these risks. Attack Insights tests your AI systems against that framework using adversarial techniques, then validates each finding so you act on real, demonstrated exposure — not theory.
Why it matters
Teams are racing to embed LLMs into customer-facing and internal products, often without security review. A single prompt-injection or excessive-agency flaw can leak data, trigger unintended actions or expose your system prompt and connected tools. Testing AI features the way an attacker would is now essential before and after you ship.
OWASP Top 10 for LLMs
Direct and indirect attempts to override instructions and hijack model behaviour.
Coaxing the model into leaking data, secrets or other users’ information.
Unsafe handling of model output that leads to XSS, SSRF or code execution.
Over-privileged agents and tools that can take unintended, harmful actions.
Extraction of hidden system prompts, guardrails and connected credentials.
Risks in models, plugins, training data and third-party AI components.
Attacks on RAG retrieval, embeddings and knowledge-base poisoning.
Resource-exhaustion and cost-amplification abuse of model endpoints.
How it works
Map your LLM features, endpoints, agents, tools and data flows — in white-, grey- or black-box mode.
Run targeted attacks for each OWASP LLM risk, probing how the system behaves under abuse.
Confirm every finding by demonstrating real impact — unproven issues are discarded, not reported.
Deliver evidence, reproduction steps and remediation guidance, then retest fixes.
Related
FAQ
It is the industry-standard list of the most critical security risks in LLM applications — including prompt injection, sensitive information disclosure, excessive agency and system prompt leakage — used to guide AI security testing.
It is adversarial testing of AI and LLM-powered applications: actively attempting prompt injection, data exfiltration, agent abuse and related attacks to find and validate real, exploitable weaknesses.
Yes. Direct and indirect prompt injection is a primary focus, alongside the other OWASP LLM Top 10 risks.
Yes. Testing is controlled, with guardrails and scope enforcement, and is designed to be safe against live systems.
It targets risks unique to LLMs — model behaviour, prompts, agents, embeddings and AI supply chain — that traditional application testing does not cover.
Penetration test your LLM applications against the OWASP Top 10 for LLMs — book a demo.
