Artificial Analysis Cyber Index
Cyber Index Alliance
Artificial Analysis Cyber Index는 Cyber Index Alliance와 협력해 개발합니다. Cyber Index Alliance는 업계 파트너를 한데 모아 기업 사이버 방어 작업에서 AI 모델의 성능을 평가하는 새로운 표준을 마련합니다. 파트너는 지수의 설계와 구현에 전문적인 의견을 제공하며, 데이터셋과 외부 연구를 직접 제공할 수도 있습니다.
Cyber Index Alliance 참여에 관심 있는 조직은 cyber@artificialanalysis.ai로 문의해 주세요.
Artificial Analysis Cyber Index
점수
Artificial Analysis Cyber Index
Artificial Analysis Cyber Index: 점수 vs. 작업당 비용
안전 차단
Artificial Analysis Cyber Index: 안전 차단
토큰 사용량
Artificial Analysis Cyber Index: 작업당 출력 토큰
비용
Artificial Analysis Cyber Index: 작업당 비용
자주 묻는 질문
Artificial Analysis Cyber Index는 AI 모델이 사이버 방어 업무를 얼마나 잘 수행하는지 나타내는 종합 지수입니다. 코드베이스에서 취약점을 발견하고, 이를 재현하고 검증하며, 기존 기능을 손상하지 않고 패치하는 방어 루프를 다루는 세 가지 사이버 보안 평가를 결합합니다.
이 지수는 CWE-Bench-AA pass@1, DeepsecBench-AA F2(세 번 실행한 결과의 중앙값), CyberGym-E2E-AA pass@1의 세 가지 평가 점수를 동일한 가중치로 평균한 값입니다. 각 점수는 평균을 내기 전에 0~100 척도로 환산합니다.
Artificial Analysis가 구현한 세 가지 벤치마크가 포함됩니다. Collinear AI의 CWE-bench를 구현한 CWE-Bench-AA, Vercel의 DeepsecBench를 구현한 DeepsecBench-AA, Berkeley Center for Responsible, Decentralized Intelligence (RDI)의 CyberGym-E2E를 구현한 CyberGym-E2E-AA입니다. 이 세 벤치마크는 코드베이스에서 약점을 찾는 스캔부터 충돌 재현과 패치 구현까지 방어 루프 전반을 다룹니다.
아니요. 이 지수는 방어 작업만 측정합니다. 모델은 애플리케이션을 감사하는 보안 엔지니어처럼 소스 코드를 기반으로 작업하며, 어떤 모델에도 실제로 작동하는 exploit을 만들도록 요구하지 않습니다. exploit 구현은 범위에 포함되지 않습니다.
아니요. Artificial Analysis Cyber Index는 사이버 보안에 초점을 맞춘 독립 지수로 발표되며, Artificial Analysis Intelligence Index에는 포함되지 않습니다.
평가 살펴보기
A composite benchmark aggregating ten challenging evaluations to provide a holistic measure of AI capabilities across mathematics, science, coding, and reasoning.
A composite measure providing an industry standard to communicate model openness for users and developers.
A private evaluation developed by Artificial Analysis for frontier agentic capability in long-horizon knowledge work, testing agents on realistic business workflows that require deliverables such as spreadsheets, presentations, and memos.
GDPval-AA v2.1 is Artificial Analysis' evaluation framework for OpenAI's GDPval dataset. It tests AI models on real-world tasks across 44 occupations and 9 major industries. Models are given shell access and web browsing capabilities in an agentic loop via Stirrup to solve tasks, with Elo ratings derived from blind pairwise comparisons.
Artificial Analysis' implementation of the APEX-Agents benchmark, testing AI agents on long-horizon, cross-application tasks in professional-services environments with realistic application tooling.
Artificial Analysis' data analysis benchmark, testing AI agents on their ability to work with spreadsheets and documents to answer quantitative questions a Business Analyst or Data Analyst would face day-to-day.
A benchmark measuring agentic task completion across simulated SaaS application environments, scoring the share of each task's objectives completed without guardrail violations.
Artificial Analysis' implementation of Harvey's Legal Agent Benchmark (LAB), testing AI agents on real-world legal work from Harvey's dataset of 120 private tasks spanning 24 legal practice areas. The agent reads case documents in a sandbox and produces legal deliverables (e.g., memos, disclosure schedules, deposition summaries), graded criterion-by-criterion by a single LLM rubric judge.
Artificial Analysis' implementation of Surge AI's GDP.pdf benchmark, testing whether language models can reason over long, real-world professional documents and satisfy detailed task-specific criteria.
Artificial Analysis' independent implementation of ServiceNow's EnterpriseOps-Gym, an agentic benchmark testing whether LLM agents can complete stateful, multi-step enterprise workflows across eight business domains via live tool use, graded on the final state of the underlying databases.
A harder 66-task benchmark of complex terminal work across software, machine learning, science, operations, security, hardware, and media, with recalibrated compute and time allowances and improved instructions, environments, and verifiers.
A 70-task benchmark of research workflows authored and reviewed by domain experts across the life, physical, earth, mathematical, and engineering sciences, each completed in a terminal and checked by its own set of tests.
A challenging benchmark measuring language models' ability to extract, reason about, and synthesize information from long-form documents ranging from 10k to 100k tokens (measured using the cl100k_base tokenizer).
A benchmark measuring factual recall and hallucination across various economically relevant domains.
A scientist-curated coding benchmark featuring 288 test set subproblems from 80 laboratory problems across 16 scientific disciplines.
A frontier-level benchmark with 2,500 expert-vetted questions across mathematics, sciences, and humanities, designed to be the final closed-ended academic evaluation.
A benchmark designed to test LLMs on research-level physics reasoning tasks, featuring 71 composite research challenges.
The most challenging 198 questions from GPQA, where PhD experts achieve 65% accuracy but skilled non-experts only reach 34% despite web access.
Artificial Analysis' implementation of IBM's ITBench benchmark, testing AI agents on Kubernetes incident root-cause analysis from offline incident snapshots. The agent inspects alerts, events, traces, and topology and identifies the contributing-factor entities (deployments, pods, namespaces, network policies, etc.) responsible for the failure.
An enhanced MMMU benchmark that eliminates shortcuts and guessing strategies to more rigorously test multimodal models across 30 academic disciplines.
A benchmark evaluating precise instruction-following generalization on 58 diverse, verifiable out-of-domain constraints that test models' ability to follow specific output requirements.
An open benchmark from Wisedocs measuring how well models reason over long, fragmented medical records, performing the multi-document synthesis claims professionals rely on when reviewing insurance and healthcare cases.
A fintech customer-support benchmark from the 𝜏-Knowledge framework that tests whether agents can navigate a large unstructured knowledge base and execute multi-step tool calls to resolve realistic banking workflows.
A verified refresh of Terminal-Bench 2.0 — 89 curated tasks across software engineering, system administration, data processing, model training, and security, with environment and instruction fixes so scores reflect agent capability rather than environment gaps.
An agentic benchmark evaluating AI capabilities in terminal environments through software engineering, system administration, and data processing tasks.
A dual-control conversational AI benchmark simulating technical support scenarios where both agent and user must coordinate actions to resolve telecom service issues.
An enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements.
A contamination-free coding benchmark that continuously harvests fresh competitive programming problems from LeetCode, AtCoder, and CodeForces, evaluating code generation, self-repair, and execution.
A 500-problem subset from the MATH dataset, featuring competition-level mathematics across six domains including algebra, geometry, and number theory.
All 30 problems from the 2025 American Invitational Mathematics Examination, testing olympiad-level mathematical reasoning with integer answers from 000-999.
A lightweight, multilingual version of MMLU, designed to evaluate knowledge and reasoning skills across a diverse range of languages and cultural contexts.