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Testing Approach. Humanbound conducts automated contextual penetration testing and quality assurance of AI agents through multi-turn adversarial and behavioral conversations, aligned with industry-standard threat taxonomies including the OWASP Top 10 for LLM Applications, the OWASP Agentic Security Top 10, MITRE ATLAS, and NIST AI Risk Management Framework categories.
Security Posture. Posture is measured across multiple dimensions: Agent Security (resilience against adversarial attacks including prompt injection, data exfiltration, privilege escalation, trust boundary violations, and agentic tool misuse) and Agent Quality (functional accuracy, boundary management, conversational intelligence, and user experience). Each dimension produces an independent score and grade. The overall posture score is the weighted composite.
Scoring. Each score ranges from 0 to 100, computed from the severity of identified vulnerabilities weighted by their current status. Grade boundaries: A (90+), B (75–89), C (60–74), D (40–59), F (<40).
Continuous Monitoring. The platform evaluates agents on a configurable schedule, automatically adjusting testing intensity based on detected signals — new vulnerabilities, regressions, behavioral drift, and coverage gaps.
Limitations. AI agents are non-deterministic systems. Results represent behavior observed at the time of testing under the specific conditions applied. Continuous monitoring is recommended to track behavioral changes over time.
This report was generated using a methodology that incorporates Large Language Model (LLM) technology in certain components of the testing and evaluation process. LLMs are based on stochastic (probabilistic) processes and are inherently non-deterministic. As a result, there is no absolute guarantee that results are fully reproducible across identical test runs, and there may be blind spots or edge cases not captured during testing. Humanbound applies best-effort scientific standards — including multi-turn adversarial conversations, score-guided adaptation, coverage-guided test selection, and statistical drift detection — to mitigate these limitations and maximise testing coverage.
This report does not constitute legal, regulatory, or compliance advice. It is a technical assessment of AI agent security and quality based on automated testing at a specific point in time. Organisations should use this report as one input among others when making security and compliance decisions.
This report was generated by Humanbound CLI — open-source AI agent security testing.
Humanbound is a trademark of AI and Me P.C. The testing methodology and report format are provided under the Apache 2.0 license. The content of this report belongs to the user who generated it.