aiqbench analysis
ThreatForest introduces an autonomous agent pipeline capable of converting source code repositories into structured attack trees mapped to standard threat frameworks like MITRE ATT&CK. The research identifies the embedding stage as a critical bottleneck for mapping accuracy, suggesting that future improvements must focus on optimizing these specific encoders rather than rearchitecting the multi-agent workflow itself. This work advances automated security engineering by enabling scalable, evidence-based mitigation synthesis without requiring scarce manual expertise.
ThreatForest: Multi-Agent Attack Tree Generation with Pluggable TTP Framework Mapping
arXiv:2607.27528v1 Announce Type: cross Abstract: Threat modeling is essential for secure software development, yet manual analysis of cloud-native architectures is slow and demands scarce security expertise. We present ThreatForest, a multi-agent system that generates structured attack trees from source code repositories, maps attack steps to adversary tactics, techniques, and procedures (TTPs) from a pluggable set of frameworks (MITRE ATT&CK, CAPEC, and cloud-specific threat matrices), and synthesizes actionable mitigations. ThreatForest decomposes threat modeling into a multi-stage agent pipeline -- repository analysis, context refinement, threat generation, parallel attack-tree construction with TTP mapping and mitigation synthesis, and report generation -- orchestrated as a directed graph with deterministic verification gates, bounded retries, and three human-in-the-loop validation points. A domain-specific sentence-transformer maps each attack step to candidate techniques by cosine similarity; we show empirically that this embedding stage, not the surrounding pipeline, is the dominant accuracy bottleneck. We evaluate ThreatForest across seven application domains on a sixteen-dimension rubric, scored by a panel of independent LLM raters with an adversarial verification pass and expert review. Panel-measured quality reaches 0.63-0.68 (on a 0-1 scale) for threat statements, attack trees, and mitigations, but only 0.29 for embedding-only TTP mapping -- a gap stable across all seven domains that isolates the binding constraint. A controlled single-call baseline on the same model more than doubles mapping defensibility, pinning the limitation on the embedding encoder rather than the multi-agent design. To our knowledge, ThreatForest is the first end-to-end system that turns a code repository into TTP-mapped attack trees with evidence-based mitigations across adversary frameworks, with a reusable framework for benchmarking such systems.
Abstract:Threat modeling is essential for secure software development, yet manual analysis of cloud-native architectures is slow and demands scarce security expertise. We present ThreatForest, a multi-agent system that generates structured attack trees from source code repositories, maps attack steps to adversary tactics, techniques, and procedures (TTPs) from a pluggable set of frameworks (MITRE ATT&CK, CAPEC, and cloud-specific threat matrices), and synthesizes actionable mitigations. ThreatForest decomposes threat modeling into a multi-stage agent pipeline -- repository analysis, context refinement, threat generation, parallel attack-tree construction with TTP mapping and mitigation synthesis, and report generation -- orchestrated as a directed graph with deterministic verification gates, bounded retries, and three human-in-the-loop validation points. A domain-specific sentence-transformer maps each attack step to candidate techniques by cosine similarity; we show empirically that this embedding stage, not the surrounding pipeline, is the dominant accuracy bottleneck. We evaluate ThreatForest across seven application domains on a sixteen-dimension rubric, scored by a panel of independent LLM raters with an adversarial verification pass and expert review. Panel-measured quality reaches 0.63-0.68 (on a 0-1 scale) for threat statements, attack trees, and mitigations, but only 0.29 for embedding-only TTP mapping -- a gap stable across all seven domains that isolates the binding constraint. A controlled single-call baseline on the same model more than doubles mapping defensibility, pinning the limitation on the embedding encoder rather than the multi-agent design. To our knowledge, ThreatForest is the first end-to-end system that turns a code repository into TTP-mapped attack trees with evidence-based mitigations across adversary frameworks, with a reusable framework for benchmarking such systems.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
K.6.5; I.2.11; D.2.4; I.2.7
arXiv:2607.27528 [cs.CR]
(or arXiv:2607.27528v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2607.27528 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 365 words
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