ACSE-Eval benchmarks LLMs on threat-modeling 100 AWS architectures and finds that GPT-4.1 and Gemini 2.5 Pro lead threat identification, while all models score below 50% on exact CWE and ATT&CK classification.
SecureFalcon: The next cyber reasoning system for cyber security
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Software vulnerabilities can cause numerous problems, including crashes, data loss, and security breaches. These issues greatly compromise quality and can negatively impact the market adoption of software applications and systems. Traditional bug-fixing methods, such as static analysis, often produce false positives. While bounded model checking, a form of Formal Verification (FV), can provide more accurate outcomes compared to static analyzers, it demands substantial resources and significantly hinders developer productivity. Can Machine Learning (ML) achieve accuracy comparable to FV methods and be used in popular instant code completion frameworks in near real-time? In this paper, we introduce SecureFalcon, an innovative model architecture with only 121 million parameters derived from the Falcon-40B model and explicitly tailored for classifying software vulnerabilities. To achieve the best performance, we trained our model using two datasets, namely the FormAI dataset and the FalconVulnDB. The FalconVulnDB is a combination of recent public datasets, namely the SySeVR framework, Draper VDISC, Bigvul, Diversevul, SARD Juliet, and ReVeal datasets. These datasets contain the top 25 most dangerous software weaknesses, such as CWE-119, CWE-120, CWE-476, CWE-122, CWE-190, CWE-121, CWE-78, CWE-787, CWE-20, and CWE-762. SecureFalcon achieves 94% accuracy in binary classification and up to 92% in multiclassification, with instant CPU inference times. It outperforms existing models such as BERT, RoBERTa, CodeBERT, and traditional ML algorithms, promising to push the boundaries of software vulnerability detection and instant code completion frameworks.
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CAI Dataset is presented as the largest described corpus of LLM-driven hacker trajectories, with the claim that operator data concentration in frontier-model providers creates a major security risk best addressed by on-premise specialized LLMs.
A multi-agent system turns code repositories into TTP-mapped attack trees, and an ablation shows the embedding encoder, not the multi-agent design, is the dominant accuracy bottleneck.
The Aw–Rascle–Zhang continuum model on directed lattice networks yields power-law spatiotemporal congestion clusters with finite-size scaling by linear system size.
Five LLMs, including GPT-4, handle many simple concurrency checks but cannot reliably verify small programs under TSO and PSO relaxed memory models.
LanG presents a governance-aware agentic AI platform for unified security operations that reports strong performance on incident correlation, rule generation, attack reconstruction, and AI safety guardrails in an open-source package.
A review of LLM-based agents as autonomous cyberattackers, arguing that they lower attack costs, scale up threats, and outpace existing defenses.
citing papers explorer
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ACSE-Eval: Can LLMs threat model real-world cloud infrastructure?
ACSE-Eval benchmarks LLMs on threat-modeling 100 AWS architectures and finds that GPT-4.1 and Gemini 2.5 Pro lead threat identification, while all models score below 50% on exact CWE and ATT&CK classification.
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Cybersecurity AI (CAI) Dataset
CAI Dataset is presented as the largest described corpus of LLM-driven hacker trajectories, with the claim that operator data concentration in frontier-model providers creates a major security risk best addressed by on-premise specialized LLMs.
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ThreatForest: Multi-Agent Attack Tree Generation with Pluggable TTP Framework Mapping
A multi-agent system turns code repositories into TTP-mapped attack trees, and an ablation shows the embedding encoder, not the multi-agent design, is the dominant accuracy bottleneck.
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Scale-free congestion clusters in large-scale traffic networks: a continuum modeling study
The Aw–Rascle–Zhang continuum model on directed lattice networks yields power-law spatiotemporal congestion clusters with finite-size scaling by linear system size.
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Assessing Large Language Models in Comprehending and Verifying Concurrent Programs across Memory Models
Five LLMs, including GPT-4, handle many simple concurrency checks but cannot reliably verify small programs under TSO and PSO relaxed memory models.
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LanG -- A Governance-Aware Agentic AI Platform for Unified Security Operations
LanG presents a governance-aware agentic AI platform for unified security operations that reports strong performance on incident correlation, rule generation, attack reconstruction, and AI safety guardrails in an open-source package.
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Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks
A review of LLM-based agents as autonomous cyberattackers, arguing that they lower attack costs, scale up threats, and outpace existing defenses.