The authors built a personal-assistant environment containing 48 multi-stage task sequences across several domains and harm categories.
Self-updating agents can improve performance while quietly weakening safety
SEABench links persistent changes to agent controllers, memories and tools with downstream safety failures that non-evolving counterparts avoid.
Research Lab
Saswat Das · Parvati Viswanathan · Daniel Donnelly · Chang Huang · Sahar Abdelnabi · Ferdinando Fioretto
University of Virginia · ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems · Tübingen AI Center
Research Digest··2 min read
Das et al.
Why this paper
From Max Planck Institute for Intelligent Systems and 3 others · Part of Safety Training Side Effects, now 5 papers
In one line
Self-evolving LLM agents can become unsafe through their own harmless updates, even without adversarial attacks.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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