This survey reviews AI-driven methods for filtering and prioritizing security alerts to combat alert fatigue, establishing a four-stage workflow taxonomy and identifying critical gaps in current resea…

CALIBURN introduces a novel, five-component streaming pipeline for intrusion detection that allows operators to specify alerting behavior using cost and budget constraints, achieving state-of-the-art…

The paper introduces SCOUT, a dynamic detector allocation framework that improves prompt-injection defense by predicting detector reliability and latency to optimize the trade-off between safety and o…

The paper proposes an end-to-end LLM framework that automates SOC operations by integrating ensemble-based threat detection, syntax-constrained query generation, and evidence-grounded incident resolut…

The paper introduces MonitoringBench, a semi-automated red-teaming methodology that generates diverse and stronger attacks, revealing that current coding-agent monitors often fail against sophisticate…

The paper introduces LAAF, a novel automated red-teaming framework, to systematically test and exploit Logic-layer Prompt Control Injection (LPCI) vulnerabilities in complex agentic LLM systems.

The paper introduces VIPER, a novel backdoor attack framework that exploits the functional fusion of malicious and benign logic within dynamic prompt architectures, demonstrating a new, high-risk thre…

The paper analyzes persistent prompt injection vulnerabilities in always-on autonomous AI agents by defining 'sleeper channels' and proposing a defense mechanism based on canonical action-instance dig…

ZERO-APT introduces a novel closed-loop adversarial framework for automated penetration testing that simulates attacks against an intelligent, real-time defending system, achieving a high attack succe…

The paper benchmarks current frontier computer-using agents against hand-crafted attacks, finding that while they are highly safe in browser tasks, this safety does not generalize to other domains lik…

The paper proposes PRAETORIAN, a novel defense mechanism for Graph Neural Networks (GNNs) that targets the intrinsic structural requirements of backdoor attacks, significantly reducing the attack succ…

The paper proposes a federated, high-throughput stream-processing framework for cross-sector threat detection and automated containment, achieving end-to-end operational convergence within 12-20 secon…

SOCpilot is a system that verifies the compliance of LLM-drafted incident response plans against mandatory policies and required procedural steps, significantly improving the reliability of AI-assiste…

The paper introduces a kill-chain canary methodology to diagnose prompt injection vulnerabilities across multi-stage LLM pipelines, revealing that write-node placement and document format are critical…

This study empirically measures the consistency and success rate of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation capabilit…

This study empirically measures the consistency and effectiveness of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation rates am…

The paper introduces a validated, consensus-labeled prompt bank that separates requests for executable malicious code (weapons) from requests for general harmful security knowledge, providing a more g…

RuleForge is an automated system that generates and validates detection rules for web vulnerabilities from structured CVE templates, significantly improving detection accuracy and reducing false posit…

The paper introduces PoisonForge, a comprehensive benchmark demonstrating that even a small number of targeted poisoned examples can significantly compromise the safety and reliability of instruction-…

The paper introduces CoT-Guard, a small, cost-effective 4B-parameter model that significantly outperforms large, expensive monitors like GPT-5 in detecting hidden objectives in code generation tasks.