Whose Credential Is It? How Owner Attribution in Agent MemoryShapes Cross-User Account Misuse
Shared long-term memory lets an LLM agent serving one user inadvertently retrieve another user's credentials, but it is unclear when such exposure leads to actual harm.
ProofPaper ↗
Key points
- We introduce a three-stage audit that separates three events: a foreign credential reaches the prompt (exposure), the agent acts with it (behavior), and the other user's account changes (state).
- In AppWorld, a store that mixes 763 past logins from all users retrieves another user's credential for 84.7 percent of required apps, yet agents rarely act on it.
- Misuse appears in a stress test where memory lacks the user's own credential and presents a single foreign one as the user's record: seven agents then use it in 80 to 94 percent of runs and change the victim's account in up to 43 percent, sometimes passing the official check.
- Naming the owner before or inside each credential line prevents most of this: with all else fixed, misuse falls from 92 to 1 of 305 paired runs, also with name-free usernames, though owner-bound notes cost 3 to 5 successful tasks per model on clean memory.
Sources (1)
- [1]Whose Credential Is It? How Owner Attribution in Agent MemoryShapes Cross-User Account MisusearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:53 AM
Shared long-term memory lets an LLM agent serving one user inadvertently retrieve another user's credentials, but it is unclear when such exposure leads to actual harm.
We introduce a three-stage audit that separates three events: a foreign credential reaches the prompt (exposure), the agent acts with it (behavior), and the other user's account changes (state).
Extractive summary: sentences quoted from the sources.
Before this
- Oct 19, 2026HarnessPro: Recursive Harness Self-Improvement with Pareto Optimization for Performance-Cost Trade-offs
- Oct 19, 2026Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy
- Oct 19, 2026AdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments
- Oct 18, 2026Symbolic Constraint Agents: Mining Formal Tool-Call Constraints to Prevent Agent Errors
- Oct 18, 2026MemPrice: Online Memory Allocation via Adaptive Pricing for Long-Horizon Agents
- Oct 17, 2026HybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents