Prompt Injection Lab
Explore instruction conflicts, prompt leakage, and simulated secret exposure in controlled LLM environments.
تقدیم به ساحت مقدس مادرم، حضرت فاطمه زهرا(سلاماللهعلیها)
Researching LLM security, prompt injection, adversarial behavior, AI agents, context security, and trustworthy autonomous systems.
LIVE RESEARCH PROTOTYPE
A living multi-agent environment where autonomous agents observe, reason, plan, act, remember, and respond to changing security conditions.
Learn, attack, observe, defend, and investigate security failures across modern AI systems.
Explore instruction conflicts, prompt leakage, and simulated secret exposure in controlled LLM environments.
Investigate poisoned retrieval context, malicious documents, and knowledge-base trust failures.
Test unsafe tool use, privilege boundaries, and agent workflow manipulation in sandboxed scenarios.
Perform structured adversarial evaluation against simulated AI systems and security controls.
Study data exposure, sensitive context leakage, and privacy failure modes in AI systems.
Reconstruct attack paths, analyze behavioral evidence, and investigate AI-assisted incidents.
Research Artifacts
The lab focuses on reproducible experiments, structured security evaluations, attack analysis, and practical AI security tooling.
An architecture-aware, evidence-informed assessment for LLM applications covering deployment, data, prompt/context security, RAG, tools, downstream outputs, controls, and residual risk.
Research focused on direct, indirect, contextual, and multi-turn prompt injection behavior.
Investigation of unsafe tool use, agent manipulation, privilege boundaries, and autonomous attack paths.
Analysis of instruction conflicts, malicious context, context poisoning, and dependency-based attacks.
An interactive multi-agent security simulation exploring cognition, trust dynamics, malicious context, rogue agents, and threat propagation.