3 articles tagged with #runtime-protection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AgentTrust is a runtime safety layer that intercepts AI agent tool calls before execution to prevent unsafe actions like accidental deletion, credential exposure, or data exfiltration. The system achieves 95-96.7% verdict accuracy across benchmarks using deobfuscation, risk chain detection, and LLM-based judgment, addressing a critical gap in AI agent safety infrastructure.
Researchers introduce ClawGuard, a runtime security framework that protects tool-augmented LLM agents from indirect prompt injection attacks by enforcing user-confirmed rules at tool-call boundaries. The framework blocks malicious instructions embedded in tool responses without requiring model modifications, demonstrating robust protection across multiple state-of-the-art language models.
Researchers identify a privacy vulnerability in AI agents that use speculative tool calls to reduce latency, where external services receive and retain inferred user intent data even after the agent abandons the speculative branch. The study proposes Speculative Tool Privacy Contracts as a runtime solution, finding that only issue-time policies suppressing or modifying calls before dispatch effectively mitigate information leakage.
