Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
Universal vulnerabili- ties in large language models: Backdoor attacks for in-context learning,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
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
-
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
-
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.