Giskard AI Testing
Open-source AI testing and quality assurance platform for LLMs and ML models. Giskard provides automated vulnerability scanning for LLMs (prompt injection, hallucination, stereotypes, toxicity), RAG pipeline evaluation, and model testing with a Python SDK. REST API and Python client for running scans, generating adversarial test cases, and retrieving vulnerability reports. Giskard Hub is the managed collaboration platform.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Apache 2.0 open-source — auditable. Test data may be sent to LLM judge APIs — consider data privacy. EU-based company. Self-hosted option for sensitive models. No SOC2 confirmed — verify for enterprise use.
⚡ Reliability
Best When
You need automated AI safety testing and red-teaming for LLMs and RAG systems, with vulnerability scanning integrated into your CI/CD pipeline.
Avoid When
You primarily need LLM output quality metrics (factual accuracy, coherence) rather than safety vulnerability scanning.
Use Cases
- • Scan AI agents for LLM vulnerabilities before production deployment — prompt injection, jailbreaks, hallucination, and bias
- • Evaluate RAG pipelines for information hazards, groundedness, and faithfulness using Giskard's automated test generation
- • Build automated safety testing into agent CI/CD pipelines — fail deployments when vulnerability scans exceed risk thresholds
- • Generate adversarial test cases for agent red-teaming using Giskard's synthetic dataset generation
- • Detect model bias and stereotypes in AI agent responses across protected characteristics
Not For
- • Traditional ML performance monitoring (accuracy, AUC) — Giskard focuses on LLM safety and quality, not performance metrics
- • Real-time production monitoring of every LLM call — Giskard runs batch safety scans, not inline evaluation
- • Teams needing SOC2 and enterprise SLA — verify Giskard Hub's current compliance status for enterprise use
Interface
Authentication
API key for Giskard Hub access. Python SDK uses key for pushing scan results. No scope granularity. Self-hosted Giskard Hub manages its own auth.
Pricing
Core library is free. LLM judge costs apply (OpenAI/Anthropic for adversarial test generation). Giskard Hub provides collaboration and tracking with free and paid tiers.
Agent Metadata
Known Gotchas
- ⚠ Vulnerability scan duration scales with model complexity and test coverage — complex RAG systems may take 30+ minutes to scan
- ⚠ LLM judge API costs for adversarial test generation can be significant — budget for GPT-4 usage in testing pipelines
- ⚠ Scan results are non-deterministic — baseline security posture requires multiple scan runs to establish statistical confidence
- ⚠ Giskard requires wrapping your LLM/agent in a giskard.Model object — integration requires refactoring if agent interface is complex
- ⚠ Some vulnerability categories (stereotypes, toxicity) depend on LLM judge quality — different judge models produce different findings
- ⚠ Giskard Hub self-hosting requires PostgreSQL and Docker — production deployment needs infrastructure management
- ⚠ EU-first company — US data residency verification needed for teams with strict US data requirements
Alternatives
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Scores are editorial opinions as of 2026-03-07.