Add Prompt Injection Mitigation Layer for Freysa Agent #14
Description
Description
Given that Freysa's challenge revolves around adversarial prompt injection and persuasive tactics, it would be valuable to implement and document a robust mitigation layer for known prompt injection techniques. This layer would serve dual purposes:
- Improve the security and resilience of the Freysa agent in handling user input.
- Create a benchmark dataset for adversarial prompts, useful for further research in AI alignment and security.
Proposed Features
- Input sanitization and canonicalization before prompt construction.
- Red-teaming dataset: Collect and tag a variety of adversarial prompts used historically in the game.
- Logging + flagging: Identify and flag repeated or structurally manipulative attempts in the prompt buffer.
- Use of a secondary classifier model to score inputs for adversarial likelihood.
Motivation
This aligns with the core theme of Freysa as a "sovereign" and adversarially trained agent. A formalized prompt defense mechanism could enhance realism and educational value, and foster deeper engagement from security researchers and alignment communities.
Additional Context
The project is already pushing the boundaries of adversarial game theory with LLMs. Integrating formal safeguards could simulate more realistic deployment scenarios and demonstrate how AI might behave in high-stakes environments (e.g., autonomous finance agents).
Resources
- OpenAI’s red team findings
- Anthropic’s constitutional AI literature
- Papers like “Universal and Transferable Adversarial Attacks on Aligned Language Models”