Randomized Controlled Trials for Phishing Triage Agent

• URL: http://arxiv.org/abs/2511.13860v1
• Date: Mon, 17 Nov 2025 19:23:08 GMT
• Title: Randomized Controlled Trials for Phishing Triage Agent
• Authors: James Bono,
• Abstract summary: This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent on analyst productivity and accuracy.<n>Agent-augmented analysts achieved up to 6.5 times as many true positives per analyst minute and a 77% improvement in verdict accuracy compared to a control group.
• Score: 1.2691047660244335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Security operations centers (SOCs) face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial (RCT) evaluating the impact of a domain-specific AI agent - the Microsoft Security Copilot Phishing Triage Agent - on analyst productivity and accuracy. Our results demonstrate that agent-augmented analysts achieved up to 6.5 times as many true positives per analyst minute and a 77% improvement in verdict accuracy compared to a control group. The agent's queue prioritization and verdict explanations were both significant drivers of efficiency. Behavioral analysis revealed that agent-augmented analysts reallocated their attention, spending 53% more time on malicious emails, and were not prone to rubber-stamping the agent's malicious verdicts. These findings offer actionable insights for SOC leaders considering AI adoption, including the potential for agents to fundamentally change the optimal allocation of SOC resources.

Related papers

• CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution [49.689452243966315]
  AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks.<n>We propose CausalArmor, a selective defense framework that computes lightweight, leave-one-out attributions at privileged decision points.<n> Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses.
  arXiv  Detail & Related papers  (2026-02-08T11:34:08Z)
• Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation [76.5533899503582]
  Large language models (LLMs) are increasingly used as judges to evaluate agent performance.<n>We show this paradigm implicitly assumes that the agent's chain-of-thought (CoT) reasoning faithfully reflects both its internal reasoning and the underlying environment state.<n>We demonstrate that manipulated reasoning alone can inflate false positive rates of state-of-the-art VLM judges by up to 90% across 800 trajectories spanning diverse web tasks.
  arXiv  Detail & Related papers  (2026-01-21T06:07:43Z)
• AgentShield: Make MAS more secure and efficient [5.105635962432747]
  AgentShield is a distributed framework for efficient, decentralized auditing.<n>AgentShield achieves a 92.5% recovery rate and reduces auditing overhead by over 70% compared to existing methods.
  arXiv  Detail & Related papers  (2025-11-28T06:55:50Z)
• Randomized Controlled Trials for Conditional Access Optimization Agent [0.9558392439655014]
  We report results from the first randomized controlled trial (RCT) evaluating an AI agent for Conditional Access (CA) policy management in Microsoft Entra.<n>Agent access produced substantial gains: accuracy improved by 48% and task completion time decreased by 43% while holding accuracy constant.<n>These findings demonstrate that purpose-built AI agents can significantly enhance both speed and accuracy in identity administration.
  arXiv  Detail & Related papers  (2025-11-17T19:33:03Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• WebGuard: Building a Generalizable Guardrail for Web Agents [59.31116061613742]
  WebGuard is the first dataset designed to support the assessment of web agent action risks.<n>It contains 4,939 human-annotated actions from 193 websites across 22 diverse domains.
  arXiv  Detail & Related papers  (2025-07-18T18:06:27Z)
• Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms [1.03121181235382]
  Large Language Model (LLM) agents face security vulnerabilities spanning AI-specific and traditional software domains.<n>This study bridges this gap through comparative evaluation of Function Calling architecture and Model Context Protocol (MCP) deployment paradigms.<n>We tested 3,250 attack scenarios across seven language models, evaluating simple, composed, and chained attacks targeting both AI-specific threats and software vulnerabilities.
  arXiv  Detail & Related papers  (2025-07-08T18:24:28Z)
• Criticality and Safety Margins for Reinforcement Learning [53.10194953873209]
  We seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users.<n>We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions.<n>We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality.
  arXiv  Detail & Related papers  (2024-09-26T21:00:45Z)
• Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
  Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception. We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
  arXiv  Detail & Related papers  (2023-10-18T11:36:42Z)
• Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents [2.5204420653245245]
  Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. This work proposes a novel Robustness Testing framework for MARL that attacks states of Critical Agents.
  arXiv  Detail & Related papers  (2023-06-09T02:26:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.