Heimdallr detects LLM-induced security risks in GitHub CI workflows by normalizing them into an LLM-Workflow Property Graph and combining triggerability analysis with LLM-assisted dataflow summarization, achieving over 0.91 F1 on threat detection in evaluation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAGE uses an agentic shadow memory to proactively detect and mitigate long-horizon threats in LLM agents by distilling safety context and assessing action risks before execution.
citing papers explorer
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Heimdallr: Characterizing and Detecting LLM-Induced Security Risks in GitHub CI Workflows
Heimdallr detects LLM-induced security risks in GitHub CI workflows by normalizing them into an LLM-Workflow Property Graph and combining triggerability analysis with LLM-assisted dataflow summarization, achieving over 0.91 F1 on threat detection in evaluation.
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MAGE: Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory
MAGE uses an agentic shadow memory to proactively detect and mitigate long-horizon threats in LLM agents by distilling safety context and assessing action risks before execution.