Adversarial AI Testing
Real Attacks. Real Findings.
FortifAI executes over 150 adversarial payloads against your AI agent endpoints — prompt injection variants, goal hijacking attempts, data exfiltration probes, and tool abuse scenarios — and reports every finding with full evidence capture in under 90 seconds.
What Is Adversarial AI Testing?
Adversarial AI testing is the systematic process of attacking an AI agent with intentionally crafted malicious inputs — adversarial payloads — to discover how it fails under attack conditions.
Unlike unit testing or functional testing, adversarial testing doesn't ask "does this agent do what it's supposed to?" It asks: "what can an attacker make this agent do that it shouldn't?"
This distinction is critical because LLM agents are probabilistic systems. They don't follow a fixed execution path — they reason. An adversary who understands how LLMs reason can craft inputs that exploit the model's tendency to follow instructions, complete patterns, or defer to apparent authority. The resulting vulnerabilities don't show up in code review or functional QA — they only appear under adversarial pressure.
Adversarial testing is distinct from but complementary to behavioral AI testing, which monitors behavioral deviations continuously in production, and AI red teaming, which is a broader exercise involving human attacker simulation.
150+ Adversarial Attacks Across 4 Categories
FortifAI's adversarial payload library covers every major attack vector against LLM agent systems — with variants for different evasion techniques and attack surfaces.
Prompt Injection Attacks
- Direct instruction override via user input
- Indirect injection through retrieved documents
- Tool output poisoning with embedded instructions
- Multi-turn injection across conversation history
- Encoded injection (Base64, Unicode, whitespace)
Goal Hijacking Attacks
- System prompt override attempts
- Role-confusion payloads (DAN, jailbreak variants)
- Objective substitution through context manipulation
- Authority impersonation (fake operator instructions)
- Semantic goal replacement via benign-looking requests
Data Exfiltration Attacks
- Credential extraction from agent memory
- PII harvesting through conversational probing
- Tool call parameter smuggling
- Outbound data encoding via innocuous outputs
- RAG-sourced sensitive data leakage
Tool & Agent Abuse Attacks
- Out-of-scope tool invocation attempts
- Write/delete operation escalation on read-only agents
- Agent-to-agent privilege escalation
- Supply chain payload injection via tool descriptions
- Recursive loop and resource exhaustion triggers
How Adversarial AI Testing Works
Connect Your Agent Endpoint
Point FortifAI at your AI agent's HTTP endpoint using the CLI config. No code changes required inside your agent.
Execute 150+ Adversarial Payloads
FortifAI fires a comprehensive library of adversarial payloads across all OWASP Agentic Top 10 categories simultaneously.
Monitor Behavioral Responses
Each payload is evaluated against expected behavior. Deviations, compliance with malicious instructions, and data leakage paths are flagged.
Generate Structured Security Report
Results map to OWASP threat categories with severity ratings, evidence capture (payload + response + reasoning), and remediation guidance.
Adversarial AI Testing — FAQs
What is adversarial AI testing?
Adversarial AI testing is the process of intentionally attacking an AI agent with crafted payloads — prompt injections, jailbreaks, poisoned tool outputs, and behavioral manipulation attempts — to discover security vulnerabilities before real attackers find them. It is the AI equivalent of penetration testing for traditional software.
How is adversarial AI testing different from behavioral AI testing?
Adversarial AI testing focuses on active attack simulation — sending malicious payloads to break agent security controls. Behavioral AI testing monitors how an agent behaves under normal and adversarial conditions, detecting deviations from expected reasoning patterns. FortifAI combines both: it fires adversarial payloads and monitors behavioral changes simultaneously.
What types of adversarial attacks does FortifAI test for?
FortifAI tests for: direct prompt injection, indirect prompt injection via RAG documents and tool outputs, jailbreak attempts, goal hijacking, system prompt extraction, credential exfiltration, tool abuse commands, memory poisoning payloads, and privilege escalation via agent chaining. 150+ payload variants are executed per scan.
Can adversarial AI testing be automated in CI/CD?
Yes. FortifAI is designed for CI/CD integration. Run npx fortifai scan in any pipeline to gate deployments on security findings. The CLI outputs structured JSON results suitable for automated pass/fail decisions and reporting dashboards.
Does adversarial testing require access to the AI model itself?
No. FortifAI performs black-box adversarial testing — it only needs access to your agent's HTTP endpoint. No model weights, source code, or internal architecture access is required, making it safe and easy to integrate with any LLM agent stack.
Find Vulnerabilities Before Attackers Do
Run adversarial AI testing against your agent stack in under 90 seconds. 150+ payloads. Structured findings. OWASP-aligned reports.