Role Grounding Attack writes attacker preferences into agent memory
Nested social posts make victim agents treat an attacker's preferences as the user's, steering tools and leaking data.
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Nested social posts make victim agents treat an attacker's preferences as the user's, steering tools and leaking data.
The training-free agent gathers evidence and reconciles conflicting judges before scoring indirect or partial personal disclosures.
In the authors' experiment, a malicious server inferred client label sets from shared guidance matrices at 50% to 84% accuracy.
Instructing the agent not to state or presuppose attributes cut leakage near the no-memory level while keeping most quality gains.
A summer-school study shows federated fine-tuning of MobileNetV4 leaks labels and stays open to client backdoors.
CIPL evaluates how much sensitive data in LLM-agent pipelines an external attacker can actually recover.
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