ATAG extends the MulVAL attack graph generator with custom Datalog facts and interaction rules to model multi-step attacks on LLM-based multi-agent applications, demonstrated on a trip planner and an email responder.
A Survey of MulVAL Extensions and Their Attack Scenarios Coverage
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abstract
Organizations employ various adversary models in order to assess the risk and potential impact of attacks on their networks. Attack graphs represent vulnerabilities and actions an attacker can take to identify and compromise an organization's assets. Attack graphs facilitate both visual presentation and algorithmic analysis of attack scenarios in the form of attack paths. MulVAL is a generic open-source framework for constructing logical attack graphs, which has been widely used by researchers and practitioners and extended by them with additional attack scenarios. This paper surveys all of the existing MulVAL extensions, and maps all MulVAL interaction rules to MITRE ATT&CK Techniques to estimate their attack scenarios coverage. This survey aligns current MulVAL extensions along unified ontological concepts and highlights the existing gaps. It paves the way for methodical improvement of MulVAL and the comprehensive modeling of the entire landscape of adversarial behaviors captured in MITRE ATT&CK.
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cs.CR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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ATAG: AI-Agent Application Threat Assessment with Attack Graphs
ATAG extends the MulVAL attack graph generator with custom Datalog facts and interaction rules to model multi-step attacks on LLM-based multi-agent applications, demonstrated on a trip planner and an email responder.