Skip to main content
Singapore
AIMenta
G

Giskard

by Giskard

Open-source LLM vulnerability scanner and red-teaming framework — automatically generating adversarial test cases to detect hallucinations, prompt injection, harmful content, and biases in APAC LLM applications before production deployment.

Visit Giskard (opens in new tab) Get a recommendation
AIMenta verdict
Recommended
5/5

"LLM vulnerability testing — APAC AI teams use Giskard to automatically scan LLM applications for hallucinations, prompt injection, harmful content, and stereotype biases before APAC production deployment using AI-generated adversarial probes."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Automated scanning: AI-generated adversarial probes across 7 APAC vulnerability categories
  • Context-aware probes: APAC domain-specific attacks tailored to application business context
  • LangChain/LlamaIndex: APAC model wrapper integration without code restructuring
  • Vulnerability report: severity scores and examples for APAC stakeholder communication
  • CI/CD gate: APAC deployment blocking on vulnerability score threshold regression
  • Open-source: self-hosted APAC scanning for data sovereignty compliance
When to reach for it

Best for

  • APAC AI engineering teams deploying customer-facing LLM applications in regulated industries who need automated pre-production vulnerability scanning — particularly APAC financial services, healthcare, and enterprise teams required to demonstrate LLM safety testing for AI governance frameworks.
Don't get burned

Limitations to know

  • ! Scan time scales with APAC LLM API latency — large scans take 10-30+ minutes
  • ! Adversarial probe quality depends on underlying LLM — may miss APAC edge cases
  • ! Smaller APAC community than general ML testing tools — fewer APAC-specific examples
Context

About Giskard

Giskard is an open-source LLM testing and vulnerability scanning framework — automatically generating adversarial test cases using AI to probe APAC LLM applications for weaknesses before production deployment. APAC AI engineering teams building customer-facing LLM applications use Giskard's scan function to identify and quantify risks across multiple vulnerability categories in a single run.

Giskard's automated vulnerability scan tests APAC LLM applications across seven risk categories: hallucinations (false information generation), prompt injection (system prompt override), harmful content (toxicity, violence, illegal advice), robustness (consistency under paraphrasing), stereotype bias (demographic biases in outputs), information disclosure (leaking training data or context), and off-topic content (refusing scope boundaries). APAC teams receive a vulnerability report with severity scores for each category and generated examples of where the LLM failed.

Giskard's test generation uses LLMs to create contextually relevant adversarial probes — rather than generic injection strings, Giskard generates APAC domain-specific attack prompts tailored to the APAC application's business context (e.g., generating financial advice probes for an APAC banking chatbot, or regulatory compliance probes for an APAC healthcare assistant). This context-aware probe generation surfaces APAC-specific vulnerabilities that generic test suites miss.

Giskard integrates with LangChain, LlamaIndex, Hugging Face, and raw Python model wrappers — APAC teams wrap any LLM component in a `giskard.Model` wrapper and run scans without restructuring APAC application code. Giskard's CI/CD integration enables APAC teams to gate deployments on vulnerability score thresholds, blocking promotion of APAC LLM versions that regress on safety benchmarks.

Beyond this tool

Where this category meets practice depth.

A tool only matters in context. Browse the service pillars that operationalise it, the industries where it ships, and the Asian markets where AIMenta runs adoption programs.

Or browse All tools · Encyclopedia · Case studies · Rankings