Memory-Induced Tool-Drift in LLM Agents
Modern LLM agents combine long-term memory for personalization with tool-calling interfaces for taking actions in the world -- a combination underpinning contemporary production systems. We study a previously unexamined failure of this combination: when personality-driven biases stored in memory (cost-consciousness, impatience, risk tolerance, etc.) silently affect tool calls in contexts where they are not applicable. We call this memory-induced tool-drift and operationalize it through MEMDRIFT, a benchmark of 105 scenarios spanning five bias dimensions and seven professional domains, generated through an automated adversarial pipeline. Across seven frontier models -- including those with...
| Authors | Mahavir Dabas, Jihyun Jeong, Ming Jin et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
Models and datasets referencing this paper
- jhjeong99/memdriftDataset
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