Find where your agent breaks before an attacker does.
Multi-turn adversarial conversations against your agent's live endpoint. The engine adapts strategy, escalates pressure, and pivots when blocked.
Adapts to every response
The engine rates each reply, pivots on refusal, presses on hesitation, and never repeats a failed approach.
18+ OWASP-mapped categories
Covers prompt injection, goal hijacking, privilege escalation, tool misuse, memory poisoning, and more across two security tiers.
Adversarial and behavioral
Attacks test how the agent fails. Behavioral QA tests how it handles legitimate use across personas and edge cases.
Every message is crafted from the agent's last response.
Most AI security tools run the same prompts against every agent and check a static rubric. A real attacker reads the refusal, finds the hesitation, and presses it. So does the Humanbound engine.
Each conversation runs against your agent's actual endpoint. The engine rates every response from 0 to 10 and rotates through eight techniques, authority, urgency, consistency traps, fabricated policies, social proof, emotional pressure, technical framing, and hypotheticals, based on how the agent reacts.
Score-guided escalation
The engine rates how close each reply is to complying. Refusals trigger a pivot to a different technique, hedging gets pressed, and partial compliance gets pushed further. It never repeats a failed approach.
Phase progression
Early turns build trust with legitimate requests. Mid-conversation deploys the primary attack, layering techniques. Late turns combine three or more techniques at maximum pressure.
Cross-conversation intelligence
Within a run, what one conversation learns is shared with the others: if authority claims work, that technique gets prioritized everywhere. On the platform, this persists across runs.
18+ attack categories mapped to OWASP.
Every test covers two tiers of OWASP-aligned categories. An independent judge scores each conversation against your agent's scope, business context, and risk level.
Tier 1: LLM Security (always runs)
Prompt injection across encodings, ciphers, steganography, and authority assertion. Sensitive information disclosure. Insecure output handling. System prompt leakage. Misinformation generation. Resource exhaustion. Human manipulation. Contextual abuse.
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Tier 2: Agentic Security (runs with or without telemetry)
Goal hijacking. Tool misuse and cross-tool injection chains. Privilege escalation. Authority boundary violations. Supply chain exploitation. Data staging. Code execution. Memory poisoning. Context manipulation. Workflow state bypass. Inter-agent exploitation. Trust exploitation. Rogue behavior.
With telemetry, the judge can verify tool calls, memory operations, and resource usage for higher-confidence Tier 2 verdicts. Works with Langfuse, LangSmith, OpenAI Assistants, Weights & Biases, Helicone and AgentOps.
Not just attacks. Behavioral QA for legitimate use.
The adversarial engine is half the picture. Behavioral QA tests your agent with legitimate scenarios and no adversarial intent: requests within and outside its scope, accuracy and consistency, clear guidance, and context across turns.
Scenarios are generated from the agent's permitted intents and run across user personas: first-time users, business professionals, non-technical users, and edge cases.
Full engine locally. Persistent intelligence on the platform.
The open-source engine runs the same attacks, judge and posture formula locally. The platform adds what builds over time: persistent intelligence, production verdicts, trends and leakage detection.
Local (OSS)
Platform
Attack engine
Judging
Findings
Run your first adversarial test.
Install the CLI, point it at your agent, and get OWASP-mapped findings in minutes. No login required.