CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models

• URL: http://arxiv.org/abs/2408.01605v2
• Date: Fri, 6 Sep 2024 18:17:07 GMT
• Title: CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models
• Authors: Shengye Wan, Cyrus Nikolaidis, Daniel Song, David Molnar, James Crnkovich, Jayson Grace, Manish Bhatt, Sahana Chennabasappa, Spencer Whitman, Stephanie Ding, Vlad Ionescu, Yue Li, Joshua Saxe,
• Abstract summary: CYBERSECEVAL 3 assesses 8 different risks across two broad categories: risk to third parties, and risk to application developers and end users. Compared to previous work, we add new areas focused on offensive security capabilities: automated social engineering, scaling manual offensive cyber operations, and autonomous offensive cyber operations.
• Score: 2.2779399250291577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We are releasing a new suite of security benchmarks for LLMs, CYBERSECEVAL 3, to continue the conversation on empirically measuring LLM cybersecurity risks and capabilities. CYBERSECEVAL 3 assesses 8 different risks across two broad categories: risk to third parties, and risk to application developers and end users. Compared to previous work, we add new areas focused on offensive security capabilities: automated social engineering, scaling manual offensive cyber operations, and autonomous offensive cyber operations. In this paper we discuss applying these benchmarks to the Llama 3 models and a suite of contemporaneous state-of-the-art LLMs, enabling us to contextualize risks both with and without mitigations in place.

Related papers

• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning [1.3863707631653515]
  Cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context.<n>Existing evaluations do not fully assess the scenarios most relevant to real-world defenders.<n>We introduce CyberSOCEval, a new suite of open source benchmarks within CyberSecEval 4.
  arXiv  Detail & Related papers  (2025-09-24T14:33:07Z)
• Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential [0.1631115063641726]
  This research provides an empirical analysis and risk profile of nine prominent Large Language Models (LLMs)<n>The RSI is an agile and scalable evaluation score, to quantify and compare the security posture and creating a risk profile of LLMs.<n>This research finds widespread vulnerabilities in the safety filters of the LLMs tested and highlights the urgent need for stronger alignment, responsible deployment practices, and model governance.
  arXiv  Detail & Related papers  (2025-09-12T19:34:10Z)
• AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions [64.85086226439954]
  We present SAFE, a benchmark for assessing the safety of embodied VLM agents on hazardous instructions.<n> SAFE comprises three components: SAFE-THOR, SAFE-VERSE, and SAFE-DIAGNOSE.<n>We uncover systematic failures in translating hazard recognition into safe planning and execution.
  arXiv  Detail & Related papers  (2025-06-17T16:37:35Z)
• SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
  Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
  arXiv  Detail & Related papers  (2025-05-23T10:56:06Z)
• Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data [2.2530496464901106]
  We present a systematic evaluation of safety risks in fine-tuned large language models (LLMs) for cyber security applications.<n>Our evaluation shows that fine-tuning reduces safety resilience across all tested LLMs.<n>We propose and evaluate a safety alignment approach that carefully rewords instruction-response pairs to include explicit safety precautions and ethical considerations.
  arXiv  Detail & Related papers  (2025-05-15T05:22:53Z)
• A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models [39.58317527488534]
  We propose a novel, instrumented risk-assessment metric that simultaneously evaluates potential threats to three key stakeholders.<n>To validate our metric, we leverage Garak, an open-source framework for vulnerability testing.<n>Results underscore the importance of multi-dimensional risk assessments in operationalizing secure, reliable AI-driven conversational systems.
  arXiv  Detail & Related papers  (2025-05-07T20:26:45Z)
• Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report [50.268821168513654]
  We present Foundation-Sec-8B, a cybersecurity-focused large language model (LLMs) built on the Llama 3.1 architecture. We evaluate it across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.
  arXiv  Detail & Related papers  (2025-04-28T08:41:12Z)
• SafeMLRM: Demystifying Safety in Multi-modal Large Reasoning Models [50.34706204154244]
  Acquiring reasoning capabilities catastrophically degrades inherited safety alignment. Certain scenarios suffer 25 times higher attack rates. Despite tight reasoning-answer safety coupling, MLRMs demonstrate nascent self-correction.
  arXiv  Detail & Related papers  (2025-04-09T06:53:23Z)
• OCCULT: Evaluating Large Language Models for Offensive Cyber Operation Capabilities [0.0]
  We demonstrate a new approach to assessing AI's progress towards enabling and scaling real-world offensive cyber operations. We detail OCCULT, a lightweight operational evaluation framework that allows cyber security experts to contribute to rigorous and repeatable measurement. We find that there has been significant recent advancement in the risks of AI being used to scale realistic cyber threats.
  arXiv  Detail & Related papers  (2025-02-18T19:33:14Z)
• LLM Cyber Evaluations Don't Capture Real-World Risk [0.0]
  Large language models (LLMs) are demonstrating increasing prowess in cybersecurity applications. We argue that current efforts to evaluate risks posed by these capabilities are misaligned with the goal of understanding real-world impact.
  arXiv  Detail & Related papers  (2025-01-31T05:33:48Z)
• ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models [0.46873264197900916]
  This paper explores the potential application of Large Language Models (LLMs) to enhance the assessment of software vulnerabilities. We develop three variants of ChatNVD, utilizing three prominent LLMs: GPT-4o mini by OpenAI, Llama 3 by Meta, and Gemini 1.5 Pro by Google. To evaluate their efficacy, we conduct a comparative analysis of these models using a comprehensive questionnaire comprising common security vulnerability questions.
  arXiv  Detail & Related papers  (2024-12-06T03:45:49Z)
• Global Challenge for Safe and Secure LLMs Track 1 [57.08717321907755]
  The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
  arXiv  Detail & Related papers  (2024-11-21T08:20:31Z)
• SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models [75.67623347512368]
  We propose toolns, a comprehensive framework designed for conducting safety evaluations of MLLMs. Our framework consists of a comprehensive harmful query dataset and an automated evaluation protocol. Based on our framework, we conducted large-scale experiments on 15 widely-used open-source MLLMs and 6 commercial MLLMs.
  arXiv  Detail & Related papers  (2024-10-24T17:14:40Z)
• SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [58.29510889419971]
  Existing benchmarks for evaluating the security risks and capabilities of code-generating large language models (LLMs) face several key limitations.<n>We introduce a general and scalable benchmark construction framework that begins with manually validated, high-quality seed examples and expands them via targeted mutations.<n>Applying this framework to Python, C/C++, and Java, we build SeCodePLT, a dataset of more than 5.9k samples spanning 44 CWE-based risk categories and three security capabilities.
  arXiv  Detail & Related papers  (2024-10-14T21:17:22Z)
• CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions [0.2999888908665658]
  Large Language Models (LLMs) have significantly advanced natural language processing (NLP) capabilities, providing versatile capabilities across various applications. However, their application to complex, domain-specific tasks, such as cyber-security, often faces substantial challenges. In this study, we introduce SecKnowledge and CyberPal.AI to address these challenges and train security-expert LLMs.
  arXiv  Detail & Related papers  (2024-08-17T22:37:39Z)
• Risks and NLP Design: A Case Study on Procedural Document QA [52.557503571760215]
  We argue that clearer assessments of risks and harms to users will be possible when we specialize the analysis to more concrete applications and their plausible users. We conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.
  arXiv  Detail & Related papers  (2024-08-16T17:23:43Z)
• Threat Modelling and Risk Analysis for Large Language Model (LLM)-Powered Applications [0.0]
  Large Language Models (LLMs) have revolutionized various applications by providing advanced natural language processing capabilities. This paper explores the threat modeling and risk analysis specifically tailored for LLM-powered applications.
  arXiv  Detail & Related papers  (2024-06-16T16:43:58Z)
• ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming [64.86326523181553]
  ALERT is a large-scale benchmark to assess safety based on a novel fine-grained risk taxonomy. It aims to identify vulnerabilities, inform improvements, and enhance the overall safety of the language models.
  arXiv  Detail & Related papers  (2024-04-06T15:01:47Z)
• Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal [0.0]
  We propose a risk assessment process using tools like the risk rating methodology which is used for traditional systems. We conduct scenario analysis to identify potential threat agents and map the dependent system components against vulnerability factors. We also map threats against three key stakeholder groups.
  arXiv  Detail & Related papers  (2024-03-20T05:17:22Z)
• The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning [87.1610740406279]
  White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. Current evaluations are private, preventing further research into mitigating risk. We publicly release the Weapons of Mass Destruction Proxy benchmark, a dataset of 3,668 multiple-choice questions.
  arXiv  Detail & Related papers  (2024-03-05T18:59:35Z)
• Mind the Gap: Securely modeling cyber risk based on security deviations from a peer group [2.7910505923792646]
  This paper proposes a new framework for cyber posture against peers and estimating cyber risk within specific economic sectors. We introduce a new top-line variable called the Defense Gap Index representing the weighted security gap between an organization and its peers. We apply this approach in a specific sector using data collected from 25 large firms.
  arXiv  Detail & Related papers  (2024-02-06T17:22:45Z)
• Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models [79.0183835295533]
  We introduce the first benchmark for indirect prompt injection attacks, named BIPIA, to assess the risk of such vulnerabilities. Our analysis identifies two key factors contributing to their success: LLMs' inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content. We propose two novel defense mechanisms-boundary awareness and explicit reminder-to address these vulnerabilities in both black-box and white-box settings.
  arXiv  Detail & Related papers  (2023-12-21T01:08:39Z)
• A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models [5.077431021127288]
  This paper addresses a gap in current research by focusing on security risks posed by large language models (LLMs) Our work proposes a taxonomy of security risks along the user-model communication pipeline and categorizes the attacks by target and attack type alongside the commonly used confidentiality, integrity, and availability (CIA) triad.
  arXiv  Detail & Related papers  (2023-11-19T20:22:05Z)
• Safety Assessment of Chinese Large Language Models [51.83369778259149]
  Large language models (LLMs) may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes. To promote the deployment of safe, responsible, and ethical AI, we release SafetyPrompts including 100k augmented prompts and responses by LLMs.
  arXiv  Detail & Related papers  (2023-04-20T16:27:35Z)

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.