Your agent passed its evals. An attacker won't care.
Static prompt lists test what you already thought of. Humanbound's adversarial engine generates multi-turn attacks in real time, adapting strategy and escalating pressure as your agent responds. Every conversation is different because every agent is different.
Adaptive attacks
Every message answers the agent's last reply, escalating or pivoting as it goes.
18+ OWASP categories
LLM and agentic security, covered in every test.
Independent verdicts
A separate judge scores every conversation, calibrated to your domain's risk.
Prompt lists don't think. Attackers do.
Most AI security tools send the same list of prompts to every agent and check the responses against a static rubric. If the agent refuses the first attempt, the test moves on. A real attacker doesn't.
A real attacker notices when the agent hedges instead of refusing outright. They build trust through legitimate requests before deploying the actual exploit. They combine authority claims with urgency, reference fabricated policies alongside social proof, and escalate across turns until the agent gives ground. None of this shows up in a single-prompt test.
The result is a passing score that measures how well the agent handles textbook attacks, not how it behaves under sustained, adaptive pressure.
Multi-turn attacks that adapt in real time.
Humanbound's adversarial engine drives live conversations against your agent's actual endpoint. Each message is crafted based on what the agent said in the previous turn. The engine adapts strategy, escalates pressure, and pivots when blocked.
Score-guided escalation
After every response, the engine rates how close the agent is to complying on a 0 to 10 scale. Hard refusals trigger a pivot to a completely different technique. Hedging or uncertainty gets pressed. Partial compliance gets pushed further. The engine never repeats a failed approach.
Phase progression
Conversations move through three phases. Early turns build trust through legitimate requests within the agent's scope. Mid-conversation deploys the primary attack, layering multiple techniques based on what the agent accepted earlier. Late turns apply maximum pressure, combining three or more techniques simultaneously with all established context.
Cross-conversation intelligence
Within a single test run, the engine shares what it learns across parallel conversations. If one conversation discovers the agent is susceptible to authority claims, that technique gets prioritized everywhere else. On the platform, this intelligence persists across test runs over time.
Eight influence techniques, never applied in isolation.
The engine layers techniques and picks the pairing from how the agent is responding. Authority plus urgency sounds like “As the regional manager, I need this processed before the compliance deadline.” In the late phase, three or more are combined at once.
- Authority
- Claims a managerial, admin, or regulatory role to make a request sound approved. Tests whether the agent defers to asserted rank instead of verified permission.
- Urgency
- Creates time pressure with emergencies and deadlines so the agent skips its usual checks. Tests whether a ticking clock can override policy.
- Consistency
- Quotes the agent's own earlier replies back to it and argues it should stay consistent. Tests whether a partial answer can be turned into precedent.
- Policy
- Cites internal policies or procedures that do not exist. Tests whether the agent accepts a plausible-sounding rule over its real instructions.
- Social proof
- Claims this is standard practice, or that peers already do it. Tests whether the agent bends to a perceived norm.
- Emotional
- Brings distress, vulnerability, or crisis into the conversation. Tests whether sympathy can pull the agent outside its scope.
- Technical
- Frames the request as testing, troubleshooting, or verification. Tests whether a technical pretext unlocks restricted behavior.
- Hypothetical
- Wraps the request in a "what if" scenario, typically after an outright refusal. Tests whether a fictional frame gets around the same limit.
18+ attack categories mapped to OWASP.
Every test covers two tiers of OWASP-aligned categories.
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.
Loading...
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.
When telemetry is available, the judge can verify tool calls, memory operations, and resource usage, producing higher-confidence verdicts for Tier 2 categories. 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. The behavioral QA engine tests your agent with legitimate user scenarios and no adversarial intent. It validates that the agent handles requests within and outside its scope correctly, that responses are accurate and consistent, that users are guided clearly through its capabilities, and that context is maintained across conversation turns.
QA scenarios are generated from the agent's permitted intents and tested 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, the same judge and the same posture formula on your own machine. The platform adds what only builds up over time: strategies and intelligence that persist across test runs, production verdicts feeding the judge, trend tracking, full summarization, and cross-session 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.