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

Browse the ExpSec AI Safety Evaluations collection for our public evaluation releases.

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 — NeurIPS 2025 Spotlight
AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents — ICLR 2025
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models — NeurIPS 2024
RobustBench: a Standardized Adversarial Robustness Benchmark — NeurIPS 2021

Work with us

We are hiring researchers and engineers. See current roles and learn more about our work at expsec.ai, or contact us at contact@expsec.ai.