Poisoning training data reshapes the loss landscape to enable targeted extraction of unseen data from LLMs with high success rates in language and vision-language models.
Vulnerabilities of foundation model integrated federated learning under adversarial threats
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces the Grounded Observer framework that applies robotics-inspired formal constructs for runtime constraint enforcement on foundation model interaction trajectories in socially sensitive domains.
Perspective paper lists secret leakage, free-rider attacks, system disruption, and misinformation as prompt-injection risks in federated military LLMs and proposes red-team wargaming plus joint policy as mitigations.
citing papers explorer
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Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs
Poisoning training data reshapes the loss landscape to enable targeted extraction of unseen data from LLMs with high success rates in language and vision-language models.
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Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains
Introduces the Grounded Observer framework that applies robotics-inspired formal constructs for runtime constraint enforcement on foundation model interaction trajectories in socially sensitive domains.
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Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation
Perspective paper lists secret leakage, free-rider attacks, system disruption, and misinformation as prompt-injection risks in federated military LLMs and proposes red-team wargaming plus joint policy as mitigations.