All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
Giskard, a Paris company that tests generative-AI applications for hallucination and security flaws, has assembled a network few testing vendors its size can match. Its trust center names AXA, BNP Paribas, Mistral, and DeepMind as users, its backers include the CTO of Hugging Face and a co-founder of Mistral AI, and it built the Phare benchmark with Google DeepMind. That reach into regulated European buyers and frontier labs is hard for a rival to assemble by writing software. Its paid Hub sells alongside a free Apache-licensed scanner that a paying customer keeps after cancelling. The durable edge is Giskard's reputation and its relationships with those labs and buyers. A buyer weighs that against how easily a team can drop the paid tool and keep the free one.
| Description | Giskard tests LLM agents for vulnerabilities, generating adversarial attacks that surface hallucinations, prompt injections, and personal information disclosure before and after deployment. It comes as an open-source tool and the Giskard Hub enterprise tier. | [f1] |
|---|---|---|
| Founded | 2021 | [f2] |
| HQ | Paris, France | [f3] |
| Latest funding | 3M EUR strategic grant (Bpifrance and European Commission) | [f4] |
| Deployment | SaaS, Self-hosted | [f5] |
| Compliance | SOC 2 Type 2 | [f5] |
| Product | What it does |
|---|---|
| Giskard | Open-source and commercial AI testing platform that red-teams LLMs and ML models with adversarial probes for prompt injection, hallucination, and sensitive-information disclosure. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Orchestration Tools Agentic orchestration tools, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients on user devices. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Gateways & Routers MCP proxies and gateways, LLM routers, outbound AI-service traffic, shadow AI egress, and model-registry traffic. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, AIBOM, and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
Giskard is an AI testing platform that red-teams LLMs and ML models with adversarial probes for prompt injection, hallucination, and sensitive-information disclosure. It is mapped to the AI Defense Matrix. [f6]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score |
|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs, demos, and third-party validation. | 4/5 |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 3/5 |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 |
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The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
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A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
pivot urgently
| Dimension | Score |
|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 2/3 |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 1/3 |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 2/3 |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 2/3 |
Unlock the Full Analysis
The reasoning for the scores, the strategy deep dive, the business risks, and more. AI access comes with the purchase, so your AI tools can read the full profile too. You keep 12 months of access.
One-time purchase: $20 per profile.
UnlockReading several? Unlock the entire catalog.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Giskard: Homepage | official | 2026-07-09 |
| f2 | Giskard about page | official | 2026-06-14 |
| f3 | Giskard about page | official | 2026-06-13 |
| f4 | Giskard milestone post on strategic funding | official | 2026-06-13 |
| f5 | AI Defense Matrix Catalog entry | other | 2026-06-13 |
| f6 | AI Defense Matrix Catalog mapping | other | 2026-06-23 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Giskard homepage | official | 2026-06-13 |
| s2 | Giskard LLM Evaluation Hub product page | official | 2026-06-13 |
| s3 | Giskard open-source evaluation and testing library on GitHub “Open-Source Evaluation & Testing library for LLM Agents” | official | 2026-06-13 |
| s4 | GitHub API repository statistics for Giskard-AI/giskard-oss “"stargazers_count": 5478” | research | 2026-06-29 |
| s5 | Giskard about page with leadership profiles “Alex Combessie Co-founder & co-CEO ... Jean-Marie John-Mathews, PhD Co-founder & co-CEO ... Matteo Dora, PhD Chief Technology Officer” | official | 2026-06-13 |
| s6 | TechCrunch on Giskard Phare hallucination study “That’s according to a new study from Giskard, a Paris-based AI testing company developing a holistic benchmark for AI models.” | press | 2026-06-13 |
| s7 | Giskard announces the Phare LLM benchmark with Google DeepMind “we are announcing a partnership between Google DeepMind and Giskard to develop a multi-lingual benchmark for Large Language Models” | official | 2026-06-13 |
| s8 | The Decoder on Giskard Phare benchmark findings “In some cases, hallucination resistance dropped by as much as 20 percent.” | press | 2026-06-13 |
| s9 | Phare A Safety Probe for Large Language Models on arXiv “Phare: A Safety Probe for Large Language Models” | research | 2026-06-13 |
| s10 | Giskard milestone post on GitHub stars and strategic funding “received strategic funding of 3M€ from the French Public Investment Bank and the European Commission” | official | 2026-06-13 |
| s11 | Giskard continuous red teaming product page | official | 2026-06-13 |
| s12 | Giskard homepage testimonials naming BNP Paribas and Decathlon “COO - BNP Paribas BCEF Catherine Mathon Giskard has streamlined our entire testing process thanks to their solution that makes AI model testing truly effortless. AI Platform Leader - Decathlon Corentin Vasseur” | official | 2026-06-13 |
| s13 | Giskard homepage testimonial naming Michelin “AI Automation - Michelin Mayank Lonare We use Giskard to test the AI assistant and supervise what it does.” | official | 2026-06-13 |
| s14 | SecurityWeek: Check Point to Acquire AI Security Firm Lakera “Check Point Software Technologies today announced plans to acquire Lakera, a Zurich and San Francisco-based company specializing in security for Agentic AI applications.” | press | 2026-06-16 |
| s15 | HiddenLayer AI security platform homepage “our platform provides AI Discovery, AI Supply Chain Security, AI Attack Simulation, and AI Runtime Security.” | official | 2026-06-16 |
| s16 | Adversa AI red teaming homepage “Adversa AI delivers continuous red teaming and remediation for the custom AI agents your business runs on.” | official | 2026-06-16 |
| s17 | Giskard Trust Center (SOC 2 Type 2 and GDPR, HIPAA in progress) “Documents 3 ... Controls 171 ... GDPR ... SOC2 type 2 report ... HIPAA (In progress)” | official | 2026-06-29 |
| s18 | CVE-2026-34172: Giskard agent server-side template injection enabling remote code execution “Prior to versions 0.3.4 and 1.0.2b1, ChatWorkflow.chat(message) passes its string argument directly as a Jinja2 template source to a non-sandboxed Environment. A developer who passes user input to this method enables full remote code execution via Jinja2 class traversal.” | other | 2026-06-29 |
| s19 | CVE-2024-52524: ReDoS in Giskard scan reported by GitHub Security Lab “Remote Code Execution (ReDoS) vulnerability was discovered in Giskard component by the GitHub Security Lab team. When processing datasets with specific text patterns with Giskard detectors, this vulnerability could trigger exponential regex evaluation times, potentially leading to denial of service” | other | 2026-06-29 |
| s20 | CORDIS: Giskard Quality Assurance for AI project funded by the European Innovation Council “helping organisations prepare for the forthcoming EU AI Act, with essential support from the European Innovation Council.” | regulatory | 2026-06-29 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Giskard homepage with compliance statement and Michelin and Decathlon testimonials “No vulnerabilities found? We refund the assessment. As a European entity, we offer native GDPR adherence alongside SOC 2 Type II and HIPAA compliance. From findings to fixes, we manage the entire remediation: we open tickets in your workflow and re-run every test until the fix is confirmed.” | official | 2026-06-18 |
| s2 | Giskard LLM Evaluation Hub: automated detection, business stakeholders, LangSmith difference, on-premise Hub “The difference between Giskard and LLM platforms like LangSmith: Giskard automatically detects critical vulnerabilities such as hallucinations, and is designed for business users, not just developers. Our team can install Giskard Hub in on-premise environments for the public sector and defense.” | official | 2026-06-18 |
| s3 | Giskard Continuous Red Teaming v2026 product page “Continuous Red Teaming v2026” | official | 2026-06-17 |
| s4 | About Giskard: team and investor bench “Alex Combessie Co-founder & co-CEO. Jean-Marie John-Mathews, PhD Co-founder & co-CEO. Matteo Dora, PhD CTO. Investors: Julien Chaumond CTO of Hugging Face, Charles Gorintin Co-founder of Alan & Mistral AI, Oscar Salazar Founding CTO of Uber.” | official | 2026-06-17 |
| s5 | GitHub API statistics for Giskard-AI/giskard-oss “"description":"Open-Source Evaluation & Testing library for LLM Agents", "stargazers_count":5478, "forks_count":478, "license":{"spdx_id":"Apache-2.0"}” | research | 2026-06-29 |
| s6 | Giskard Trust Center (named users, backers, SOC 2 Type 2, GDPR, HIPAA in progress) “We provide the testing infrastructure used by AI teams at AXA, BNP Paribas, Mistral and DeepMind to validate LLM quality and security. Backed by Elaia, Bessemer and the CTO of Hugging Face. Documents 3 Controls 171 GDPR SOC2 type 2 report HIPAA (In progress).” | official | 2026-06-29 |
| s7 | Giskard announces Phare LLM benchmark with Google DeepMind at the Paris AI Summit “During the Paris AI Summit, Giskard launches Phare, a new open and independent LLM benchmark to evaluate key AI security dimensions, with Google DeepMind as research partner. Giskard will maintain a dedicated hold-out dataset for assessments, preventing contamination of model training.” | official | 2026-06-18 |
| s8 | Phare: A Safety Probe for Large Language Models (arXiv preprint, Giskard CTO Matteo Dora a co-author) “We introduce Phare, a multilingual diagnostic framework to probe and evaluate LLM behavior across hallucination and reliability, social biases, and harmful content. Our evaluation of 17 state-of-the-art LLMs reveals patterns of systematic vulnerabilities. Authors include Matteo Dora.” | research | 2026-06-17 |
| s9 | TechCrunch on Giskard Phare hallucination study “That is according to a new study from Giskard, a Paris-based AI testing company developing a holistic benchmark for AI models.” | press | 2026-06-17 |
| s10 | The Decoder on Giskard Phare benchmark findings “In some cases, hallucination resistance dropped by as much as 20 percent. Larger models from Anthropic and Meta showed much less sensitivity to exaggerated user certainty.” | press | 2026-06-17 |
| s11 | Giskard milestone post on strategic funding from Bpifrance and the European Commission “received strategic funding of 3M€ from the French Public Investment Bank and the European Commission” | official | 2026-06-13 |
| s12 | CVE-2026-34172: Giskard agent server-side template injection enabling remote code execution “Prior to versions 0.3.4 and 1.0.2b1, ChatWorkflow.chat(message) passes its string argument directly as a Jinja2 template source to a non-sandboxed Environment. A developer who passes user input to this method enables full remote code execution via Jinja2 class traversal.” | other | 2026-06-29 |
| s13 | CVE-2024-52524: ReDoS in Giskard scan reported by GitHub Security Lab “Remote Code Execution (ReDoS) vulnerability was discovered in Giskard component by the GitHub Security Lab team. When processing datasets with specific text patterns with Giskard detectors, this vulnerability could trigger exponential regex evaluation times, potentially leading to denial of service” | other | 2026-06-29 |
| s14 | CORDIS: Giskard Quality Assurance for AI project funded by the European Innovation Council “helping organisations prepare for the forthcoming EU AI Act, with essential support from the European Innovation Council.” | regulatory | 2026-06-29 |
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