What we are
EEP publishes evidence-based data and analysis of emerging AI tools. Our work pairs hands-on technical expertise with interpretive policy: we measure how models actually behave under realistic conditions, and we explain what those measurements mean for the people who have to make decisions about deployment.
We are dedicated to the thorough exploration of models and their safety. That means practical, repetitive investigation rather than one-off demonstrations — the unglamorous work of running an experiment again, varying one factor at a time, and reporting the result whether or not it confirms the hypothesis. We believe holistic security is built this way, and that the methods matter as much as the conclusions.
Full reports are hosted on our Substack; this site carries standalone summaries and the occasional interactive visualization.
What we study
Prompt injection & indirect injection
Direct and content-borne instruction attacks against LLM-backed systems, and the conditions under which they succeed.
Retrieval security & RAG poisoning
Whether retrieval pipelines act as a security control or an attack surface — and which layer is actually load-bearing.
Agent evaluation
How autonomous and tool-using agents behave across cooperative, constrained, and adversarial settings.
Evaluation methodology
Harness design, scoring, and the failure modes of evaluations themselves — including when a harness rewards the appearance of correctness.
How we work
- Empirical first
- Every claim is anchored in data we collected. We run real models — open and frontier — against concrete threat models, and report the measurements, including negative and null results.
- Reproducible by design
- Harnesses, prompts, and code are published alongside findings so peers can re-run, challenge, and extend them. A result that cannot be reproduced is a hypothesis, not a finding.
- Taxonomy-aligned
- Findings are mapped to the OWASP LLM Top-10 and MITRE ATLAS so they slot directly into the frameworks practitioners already use.
- Policy-legible
- Technical results are translated into interpretive policy — language a decision-maker can act on without a model card in hand.
How findings are classified
Each report is tagged against two established taxonomies, so a reader can connect a specific result to the broader threat landscape and to their own controls.
OWASP LLM Top-10
The community-standard catalogue of the most critical vulnerabilities in LLM applications.
MITRE ATLAS
A knowledge base of adversarial tactics and techniques against AI-enabled systems.
Who we are
A small group doing careful, repeatable work.