Automated adversarial testing of your LLM applications and AI agents, covering the prompts, retrieval, memory, tools and workflows that shape how they behave — rerun on demand or on every change.
Comprehensive adversarial coverage tailored to how your system interacts—from stateless LLM prompts to multi-step autonomous agent workflows.
Adversarially test the behavior introduced by an LLM within an application, including how it processes user instructions, retrieves context and returns output.
Test how an agent can be manipulated through its goals, instructions, memory, tools, permissions and connected real-world workflows.
Systematically evaluate your generative AI systems against recognized industry standards. Infiltra stress-tests prompts, RAG retrieval pipelines, and agent permissions against the OWASP Top 10 for LLMs and Agentic Applications.
Every test scenario actively provokes and stress-tests your model using real-world adversarial jailbreaks, multi-turn elicitation, and indirect prompt injection.
Confirmed vulnerabilities include full prompt-response interaction transcripts, exact payload sequences, and deterministic traces for immediate developer remediation.
Saved test profiles enable 1-click delta retesting after prompt adjustments, plus automated regression testing when foundation models or tool permissions change.
Every assessment starts a lifecycle. Findings need retesting, and AI behavior must be revalidated whenever prompts, models, RAG context, or tool permissions change.
A material change to the underlying foundation model can alter how instructions, guardrails, and tool-use decisions behave—even when your application code remains unchanged. A regression red-team run confirms that the application or agent still behaves securely within intended boundaries.
AI red teaming is adversarial security testing designed specifically for generative AI systems. For LLM applications and autonomous AI agents, it means attacking the prompts, retrieved context, memory, tools, and permissions that shape system behavior—discovering where models can be coerced into exposing confidential data, bypassing guardrails, or executing unauthorized actions.
Infiltra automates this with intelligent red-team agents that execute OWASP-aligned attack scenarios, safely reproduce verified impact with complete interaction traces, and rerun identical profiles after prompt updates or model changes.
Infiltra unifies both software perimeters and generative AI deployments on one platform: this engine red-teams LLM Applications and AI Agents, while Web App & API Penetration Testing validates business logic, authentication, and conventional application perimeters.
Findings map directly to recognized OWASP categories, providing structured, defensible reporting that risk, engineering, and compliance teams can act on immediately.
| OWASP Category | What Infiltra Red Team Tests |
|---|---|
| LLM01: Prompt Injection |
Direct & Indirect Injection
Direct user prompt overrides, instructions hidden in retrieved context (RAG), and multi-turn guardrail bypass attacks.
|
| LLM02: Sensitive Information Disclosure |
Data Leakage & PII Probing
Adversarial extraction of confidential training records, personal identifiable information (PII), API tokens, and internal credentials.
|
| LLM05: Improper Output Handling |
Downstream Execution Exploits
Payloads designed to trigger XSS, SQL injection, SSRF, or remote code execution when model responses pass into downstream systems.
|
| LLM06: Excessive Agency |
Tool Misuse & Privilege Escalation
Manipulating autonomous agents into calling unauthorized tools, exceeding API scopes, or triggering destructive real-world actions.
|
| LLM07: System Prompt Leakage |
System Instruction Extraction
Social engineering and meta-prompting payloads that extract proprietary instructions, internal guardrails, and embedded secrets.
|
| LLM08: Vector & Embedding Weaknesses |
RAG Poisoning & Context Tampering
Simulated poisoning of vector knowledge bases and similarity search indexes to coerce biased, hallucinated, or malicious outputs.
|
| Agentic & MCP Threats |
Multi-Turn Agent Hijacking
Testing Model Context Protocol (MCP) servers, agent-to-agent delegation boundaries, persistent memory poisoning, and goal drift.
|
Repeatable red-team results give risk, compliance and audit teams documented evidence of how AI systems are tested.
Supports the expectation that AI system security, trustworthiness, and resilience are systematically evaluated and documented.
Test results and delta retests document how AI risks are assessed, treated, and monitored within your AI management system.
Generates technical evidence supporting accuracy, robustness, and cybersecurity requirements for high-risk AI deployments.
Supports the Australian standard's core guardrail on rigorous testing of AI systems before and after production deployment.
Our subscription model allows you to run on-demand red-team assessments for new releases, remediation retests, and regression testing as your prompts, models, and workflows evolve.