Add ExpSec organisation card

README.md

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 title: README
-emoji: 🔥
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-colorTo: indigo
+emoji: 🛡️
+colorFrom: indigo
+colorTo: red
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-short_description: Exponential Security Labs — securing the age of agency.
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<p align="center">
+  <img
+    src="https://huggingface.co/spaces/ExpSec/README/resolve/main/assets/exp-horizontal-cream-rounded-1024.png"
+    alt="Exponential Security Labs"
+    width="820"
+  >
+</p>
+
+<p align="center"><strong>Securing the age of agency.</strong></p>
+
+<p align="center">
+  <a href="https://expsec.ai">Website</a> ·
+  <a href="https://huggingface.co/ExpSec">Hugging Face</a> ·
+  <a href="https://x.com/expsecai">X</a> ·
+  <a href="https://www.linkedin.com/company/126363959">LinkedIn</a> ·
+  <a href="mailto:contact@expsec.ai">Contact</a>
+</p>
+
+## About
+
+Exponential Security Labs is an AI safety and security research company. We study AI systems that act through tools and multi-step workflows, and build automated red-teaming and adaptive guardrail methods for them.
+
+Our team brings nearly a decade of work in adversarial robustness and multimodal AI safety. We publish evaluation datasets and develop methods for identifying failures, measuring harmful assistance, and testing safeguards against adaptive attacks.
+
+## What we work on
+
+- **Agent safety evaluation** — benchmarks and evaluation methods for tool-using, multi-step AI systems.
+- **Automated red-teaming** — agents and methods that identify vulnerabilities in models, tools, and agent workflows.
+- **Adaptive guardrails** — defences designed to respond to changing attacks and deployment contexts.
+- **Adversarial robustness** — reliable behaviour under malicious or unexpected inputs across language and vision systems.
+
+## On the Hub
+
+### [Sovereign-Jbreak](https://huggingface.co/datasets/ExpSec/Sovereign-Jbreak)
+
+A regional safety evaluation dataset for testing whether a language model gives actionable help towards a harmful objective. The initial release contains expert-authored evaluation items for Europe and India, with goal-specific rubrics and no model responses or attack trajectories.
+
+> **Content warning:** This dataset describes high-risk misuse. Read its dataset card and responsible-use guidance before downloading or using it.
+
+## Selected research from our team
+
+- [OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents](https://arxiv.org/abs/2506.14866) — NeurIPS 2025 Spotlight
+- [AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents](https://arxiv.org/abs/2410.09024) — ICLR 2025
+- [JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models](https://arxiv.org/abs/2404.01318) — NeurIPS 2024
+- [RobustBench: a Standardized Adversarial Robustness Benchmark](https://arxiv.org/abs/2010.09670) — NeurIPS 2021
+
+## Work with us
+
+We are hiring researchers and engineers. See current roles and learn more about our work at [expsec.ai](https://expsec.ai/#hiring), or contact us at [contact@expsec.ai](mailto:contact@expsec.ai).