Empirical analysis of 444 iOS apps using dynamic traffic interception found 282 leaking LLM API keys across ten providers, with only 28% remediation after three months.
Vellmes: A high- interaction ai-based deception framework
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Honeyval evaluates LLM HTTP honeypots with AI attackers and shows they produce longer interactions, lower detection rates, and cost advantages over rule-based baselines.
Large-scale SSH honeypot deployment shows 99.23% of authenticated sessions are non-interactive, suggesting most attacks do not involve shell interaction.
AdvancedShelLM deploys a manager-worker multi-LLM architecture and stateful filesystem for SSH honeypots, reporting up to 99% unit-test pass rates and evidence that its outputs alter real attacker behavior in deployment.
A multi-agent system with hybrid RAG and two new enforcement mechanisms shows strong results on semantic extraction phases of IT-Grundschutz but weak results on logical reasoning phases when evaluated against a BSI case study.
citing papers explorer
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Mind your key: An Empirical Study of LLM API Credential Leakage in iOS Apps
Empirical analysis of 444 iOS apps using dynamic traffic interception found 282 leaking LLM API keys across ten providers, with only 28% remediation after three months.