StingerAutomate AI Vulnerability Discovery
StarfortEnforce Real-Time AI Guardrails
Research & BenchmarksSet the Standard for AI Safety
We don't wait for incidents. Our research teams identify next-generation threats at top AI conferences — before they appear in the wild.
We think like attackers. By proactively stress-testing your AI with millions of scenarios, we expose vulnerabilities that traditional testing misses.
Static rules break. Our guardrails continuously learn and adapt to your enterprise environment — blocking threats in real time without slowing down your AI.
Red teaming and guardrails in a single platform. One integration, complete lifecycle coverage from development to production.
Proactive identification of model vulnerabilities and hallucinations with thousands of automated scenarios.
Proxy-level blocking of malicious prompts and real-time masking of sensitive data.
From proprietary LLMs and custom-built agents to commercial APIs like ChatGPT and Gemini — even coding agents like Claude Code and GitHub Copilot. One platform that secures them all.
Deploy as cloud SaaS or fully isolated on-premise — tailored to your data governance and compliance requirements.
AIM Intelligence has joined SK Telecom’s consortium for the government’s “AI for Everyone” project, taking responsibility for AI security. The company will provide AI red teaming and guardrail technologies to detect and prevent risks such as data leaks, policy violations, prompt attacks, and abnormal AI behavior. Its role will extend beyond chatbot security to AI agents and physical AI, helping establish safer and more reliable AI services for the public.
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We built the first systematic framework for testing whether LLMs follow organization-specific policies. Across 5,920 queries and seven frontier models, the result is a fundamental asymmetry: models handle legitimate requests with over 95% accuracy but refuse only 13–40% of direct policy violations — and as little as 3% under adversarial framing.
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We built the first country-grounded, cross-cultural safety benchmark: 5,500 native-language test cases across 10 countries, evaluated on 10 frontier and 27 local models. Country-specific attacks push frontier ASR from 34.5% (US) to 57% (UAE) — and much of what looks like 'safety' in local models is just failure to understand the prompt.
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