Loading ToolBench
Behavioral safety, vulnerability probing, and product-risk auditing platform for Generative AI & Large Language Models. Detects hallucinations, fake confidence, context loss, instruction drift, persuasive manipulation, and recursive reasoning collapse.
Server has 7 tools with complete input schemas and descriptions. Naming follows verb_noun convention (audit_, get_, log_, scan_, run_). Descriptions are substantive (100-250 chars), explaining WHAT and WHEN. However, parameter descriptions lack format constraints (e.g., severity enum not enforced in schema), output schemas are not formally documented, and error handling is minimal. No tool annotations (readOnlyHint/destructiveHint). Composition is sound, each tool has one responsibility. Missing: pagination guidance, per-item error reporting, and recovery hints in error responses.
Audit any LLM prompt-response pair for 6 behavioral failure modes: hallucinations (invented citations/facts), fake confidence (epistemic arrogance), context loss (amnesia), instruction drift (negative constraint violations), manipulation (urgency/steering), and recursive reasoning collapse (circular logic). Returns structured verdict, confidence scores, evidence, and mitigations.
Retrieve the formal AI Failure Taxonomy definitions, severity scores (1-10), product risk implications, and concrete engineering mitigations. Pass optional failure_type ('hallucinations', 'fake_confidence', 'context_loss', 'instruction_drift', 'manipulation', 'recursive_reasoning_collapse') or leave empty for full taxonomy.
Generate the real-time AI product risk scorecard, incident distribution across failure categories, aggregate vulnerability score, and top compliance risk areas.
Record a detected AI safety or behavioral failure incident into the persistent observatory database for compliance tracking and risk index calculation. Failure types: 'hallucinations', 'fake_confidence', 'context_loss', 'instruction_drift', 'manipulation', 'recursive_reasoning_collapse'.
Execute the automated reproducible evaluation benchmark suite across all 6 AI failure modes and return detailed pass/fail test results.
Output schemas not formally documented. Tools return dicts with fields like 'audit_verdict', 'detected_failures', 'category_breakdown' but no JSON Schema definition visible in code. LLMs cannot plan downstream calls without knowing return structure.
Parameter 'severity' in log_safety_incident accepts free-form string with default 'medium', but no enum constraint in schema. Description says 'Severity level (default: medium)' but does not list valid values. LLM may pass invalid values like 'critical' or 'low' without validation.
Error responses lack recovery guidance. When get_failure_taxonomy receives unknown failure_type, it returns {'error': '...'} with available types listed, which is good. But audit_prompt_response and other tools do not show explicit error handling for invalid inputs. No guidance on retryability or next steps.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 62 | 2026-07-28+ | v2 |
Execute reproducible adversarial benchmark tests on LLMs for specific failure modes ('hallucinations', 'fake_confidence', 'context_loss', 'instruction_drift', 'manipulation', 'recursive_collapse') or run 'all' test suites.
Scan a multi-turn conversation dialogue (JSON array of {role: 'user'|'assistant', content: '...'}) to detect working memory context degradation, conversational amnesia, and progressive instruction drift.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). log_safety_incident is destructive (writes to database) but not marked. audit_prompt_response is read-only but not annotated. Agents cannot infer safety properties without explicit hints.
Parameter 'turns_json' in scan_multi_turn_conversation expects JSON string but description does not specify format or example structure. LLM must infer that it should be a JSON array of {role, content} objects. Explicit format guidance needed.