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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAI cybersecurity benchmarks measure specific behaviors and tasks—not one universal ability to “hack.” A score might mean a model refused a harmful request, solved a capture-the-flag challenge, triggered a crash, exploited a sandboxed app, or completed part of an operation in an emulated network. To understand what a result says about an AI, look at the task, success rule, tools, prompts, environment and attempt budget behind it.
What does an AI cybersecurity benchmark actually measure?
“Cybersecurity benchmark” is an umbrella term. Some evaluations test whether a model will assist with harmful requests; others measure whether it can complete an offensive or defensive task. Those results answer different questions and should not be collapsed into one hacking score.
| Evaluation type | What it tests | What counts as success | What the result does not establish |
|---|---|---|---|
| Safety and refusal | Whether a model complies with cyberattack requests, rejects benign ones, or is vulnerable to prompt injection or code-interpreter abuse | A classified compliance, refusal or false-refusal outcome | Whether the model can independently exploit a target |
| CTF challenge | Solving a prepared capture-the-flag task | Submitting the required flag | How the model would perform against an unprepared live target |
| Vulnerability test | Reproducing or exploiting a flaw in code or a vulnerable application | A crash, verified exploit or other benchmark-defined outcome | Whether the same result transfers to remote, defended systems |
| Cyber range | Chaining actions across an emulated network toward a scenario objective | Completion of the specified objective or stage | How broadly the result applies beyond that range and scenario |
| Defensive analysis | Tasks such as malware analysis or threat-intelligence reasoning | Task-specific analysis performance | Offensive exploitation ability |
How are AI models tested on cybersecurity tasks?
Safety, misuse and over-refusal
Meta’s CyberSecEval 2 tests whether models comply with cyberattack requests, whether they wrongly refuse benign requests, and risks involving prompt injection and code-interpreter abuse. It also includes vulnerability-exploitation tests, so a reference to a “CyberSecEval score” needs to specify which dimension is meant. Meta’s April 18, 2024 overview describes a safety-utility tradeoff: conditioning a model to refuse unsafe prompts can also make it falsely refuse benign ones, reducing usefulness.
Finding or exploiting vulnerabilities
One test may ask a model to produce an input that triggers a vulnerability; another may give an agent a vulnerable application and check for a working exploit. The result depends on the benchmark’s verification rule. Google Project Zero describes a crash/no-crash criterion for CyberSecEval 2 vulnerability tests and argues that a one-shot prompt about a single file can miss capabilities that emerge through iterative investigation.
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CVE-Bench, described in an ICML 2025 paper, uses a sandbox framework containing vulnerable web applications based on critical-severity CVEs. Its authors report that the state-of-the-art agent framework tested exploited up to 13% of vulnerabilities in that benchmark setup. “Up to” and “in that setup” matter: this is not an estimate of the share of real-world systems an AI could hack.
Capture-the-flag challenges
CTF benchmarks use bounded challenges with a concrete goal, commonly submitting a flag. The US and UK AI Safety Institutes’ December 2024 report describes a US AISI evaluation of o1 on 40 Cybench tasks: o1 achieved 45% Pass@10, compared with 35% for the best reference model evaluated. Pass@10 reports success when at least one of up to ten attempts solves a task; it is not the same as success on a single attempt.
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Cybench’s 40 tasks came from four professional-level CTF competitions and covered cryptography, web, forensics, reverse engineering, binary exploitation (“pwn”) and miscellaneous challenges. First-solve time can help convey challenge difficulty, but the report cautions that times are not fully comparable across competitions. Its implementation also used the Inspect agent framework and fixed challenge bugs, details that matter when comparing results with another run.
Tool-using vulnerability research
Project Zero’s Project Naptime evaluates an agent interacting with a codebase through specialized tools and iterative hypotheses. In selected CyberSecEval 2 buffer-overflow tasks, Google reported GPT-4 Turbo scores of 0.05 for the original-paper result and 1.00 for Naptime@10 and Naptime@20. These are setup-specific reported values, not a general rate for solving vulnerabilities. The comparison illustrates how multiple tool-supported trajectories can change the measured outcome compared with a single completion.
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Project Zero says this approach relies on robust tool use and reports results only for models with demonstrated proficiency in tool use; it also notes that prompt wording affected performance. The agent, tools and prompt are therefore part of the tested system, not incidental details that can be ignored when attributing a result to the base model.
Multi-step cyber ranges
A cyber range places an agent in an emulated network and measures a longer workflow, such as planning, exploiting vulnerabilities or misconfigurations, and chaining actions toward an objective. OpenAI describes this kind of evaluation in its GPT-5.2-Codex addendum. The addendum also illustrates how tightly a reported configuration can be bounded: its CVE-Bench version 1.0 run used 34 of 40 challenges, a zero-day prompt configuration, no source-code access to the target application and Pass@1 over three rollouts. Those settings define the run; they do not by themselves provide a universal comparison with other benchmarks.
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The 2026 AgentCyberRange preprint describes 110 vulnerabilities across 15 real web applications and eight enterprise-like ranges containing 156 internal hosts. It reports web exploitation and post-exploitation separately. GPT-5.5 with Codex solved 16.1% of web exploitation tasks and 31.7% of post-exploitation tasks; with more concrete hints, the reported results were 33.0% and 46.3%, respectively. The hinted and unhinted figures describe different conditions, and the preprint’s numbers should not be blended into a single capability estimate.
Defensive cybersecurity tasks
Offensive evaluations do not cover all AI cybersecurity work. Meta’s CyberSOCEval, part of CyberSecEval 4, assesses defensive tasks including malware analysis and threat-intelligence reasoning. Performance on those tasks is evidence about defensive analysis, not a proxy for exploitation skill.
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Does a high score mean an AI can hack real systems?
Not on its own. A benchmark score establishes performance on the benchmark’s tasks under its rules and conditions. A CTF flag, a sandbox exploit and a multi-host range objective are distinct outcomes. Even a realistic emulated environment covers only the hosts, vulnerabilities, defenses and goals represented in that scenario; it cannot stand in for every live system.
Results also depend on the evaluation harness. A model may be tested alone or as an agent with tools, given source code or only remote access, prompted with a broad task or a concrete hint, and allowed one attempt or many. Changing these conditions can change the result without changing the underlying model. OpenAI’s Preparedness Framework, as quoted in the GPT-5.2-Codex addendum, defines “high cybersecurity capability” in terms of removing bottlenecks to scaling cyber operations, including automating end-to-end operations against reasonably hardened targets or automating discovery and exploitation of operationally relevant vulnerabilities. That is a broader standard than completing a bounded benchmark task.
How should you compare benchmark scores?
Before comparing two percentages, check whether the evaluations ask the same question and use comparable conditions. If an article or report does not provide these details, treat the comparison as incomplete rather than assuming the numbers form a leaderboard.
- Task and target: Is it a knowledge question, CTF challenge, vulnerability reproduction, sandboxed web app or multi-host range?
- Success rule: Does success mean a correct answer, a refusal or compliance label, a crash, a verified exploit, a submitted flag or a scenario objective?
- Environment: Is the task synthetic, drawn from a public challenge, run in a sandbox or set in an emulated enterprise network?
- Agent setup: Is the model working alone or with an agent framework and tools? Is target source code available?
- Prompt and disclosure: Does the prompt say “zero-day,” describe the vulnerability, or provide a concrete hint?
- Attempts and budget: Is the score Pass@1 or Pass@10? How many rollouts, tool calls, messages or how much time are allowed?
- Coverage and difficulty: How many tasks are included, what kinds of tasks are they, and how was difficulty assigned?
- Version and date: Which benchmark release, model snapshot and evaluation harness were used?
These questions explain why the CVE-Bench, Cybench and AgentCyberRange percentages above are not directly comparable: they use different tasks, success criteria, environments and evaluation conditions. Attribute every quoted figure to its benchmark, evaluator and year.
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