BrowseSafe++
Detectors judge 38,675 matched attack and control web pages built from real production prompt injections, in HTML, Markdown, accessibility-tree and screenshot views. Scored by F1.
24 attack types and 80 languages. The 9,175-pair test split is held for the board, which lists 17 text detectors and adaptive attacks on seven browser agents.
In the authors’ words
Browser agents need to make decisions and execute actions on behalf of users while consuming untrusted webpages, creating opportunities for malicious instructions embedded in webpages to redirect legitimate tasks. We introduce BrowseSafe++, which extends BrowseSafe from 14,719 pages to 38,675 pairs of pages. Most of its prompt injections were observed in the wild on production websites. We keep each injection unchanged and rewrite the sensitive parts of the page around it to protect privacy. Each attacked page has a clean copy of the same page as its control. Both are provided as HTML, Markdown, an accessibility tree, and a screenshot. Every pair includes a user task and its expected answer. The benchmark includes twelve types of websites, from news articles to shared documents and forums, with 24 attack types appearing in 18 different locations on a page. We use these paired pages to train BrowseSafe-Guard, two 0.8B-parameter detectors for text and screenshots. Both achieve higher F1 scores than the detector based on frontier models, while keeping false-positive rates on benign pages and inference cost low. BrowseSafe-Adapt revises benchmark injections from browser-agent feedback, and attacks that succeed on only a few percent of tasks when replayed directly reach up to 95.41% success once adapted.
- Level
- University Undergraduates and entry-level professionals
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
GPT-6 Astra leads with 91.5%
Best score per entry from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
- 91.5 GPT-6 Astra
- 89.9
Gemini 3.1 Pro
- 89.3 GPT-5.5
- 89.2 Claude Fable 5.1
- 89.0
Qwen3.7-Max
- 88.4 Jev 1.13
- 88.4 Claude Sonnet 5
- 85.3 Claude Opus 5.5
- 80.9
Gemini 3.1 Flash-Lite
- 76.1 GPT-5.4 Nano
- 75.2
DeepSeek-V3.1
- 72.8 Claude Haiku 4.5
- 71.0 gpt-oss-20b
- 65.7
Qwen3-30B-A3B-Instruct
- 59.7
Mistral NeMo
- 23.1
Llama 3.1-8B
- 1 GPT-6 Astra 91.5%
- 2
Gemini 3.1 Pro 89.9%
- 3 GPT-5.5 89.3%
- 4 Claude Fable 5.1 answered pages only 89.2%
- 5
Qwen3.7-Max 89%
- 6 Jev 1.13 88.4%
- 7 Claude Sonnet 5 88.4%
- 8 Claude Opus 5.5 85.3%
- 9
Gemini 3.1 Flash-Lite 80.9%
- 10 GPT-5.4 Nano 76.1%
- 11
DeepSeek-V3.1 75.2%
- 12 Claude Haiku 4.5 72.8%
- 13 gpt-oss-20b 71%
- 14
Qwen3-30B-A3B-Instruct Jul 2025 65.7%
- 15
Mistral NeMo 59.7%
- 16
Llama 3.1-8B 23.1%
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 91.5% | Sep 2026 | Official leaderboard | |
| 2 | | 89.9% | Feb 2026 | Official leaderboard | |
| 3 | GPT-5.5 | 89.3% | Apr 2026 | Official leaderboard | |
| 4 | Claude Fable 5.1 | 89.2% | answered pages only | Sep 2026 | Official leaderboard |
| 5 | | 89% | May 2026 | Official leaderboard | |
| 6 | Jev 1.13 | 88.4% | Official leaderboard | ||
| 7 | Claude Sonnet 5 | 88.4% | Jun 2026 | Official leaderboard | |
| 8 | Claude Opus 5.5 | 85.3% | Sep 2026 | Official leaderboard | |
| 9 | | 80.9% | Mar 2026 | Official leaderboard | |
| 10 | GPT-5.4 Nano | 76.1% | Mar 2026 | Official leaderboard | |
| 11 | | 75.2% | Aug 2025 | Official leaderboard | |
| 12 | Claude Haiku 4.5 | 72.8% | Oct 2025 | Official leaderboard | |
| 13 | gpt-oss-20b | 71% | Aug 2025 | Official leaderboard | |
| 14 | | 65.7% | Jul 2025 | Official leaderboard | |
| 15 | | 59.7% | Jul 2024 | Official leaderboard | |
| 16 | | 23.1% | Jul 2024 | Official leaderboard |
The best score, 91.5%, costs $0.11 a task
Runs on the cost frontier score higher than any run that's cheaper. Qwen3.7-Max comes within 2.5 points of the best for $0.02 a task, 6 times cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| GPT-6 Astra | 91.5% | $0.11 | 79 | ||
| | 89.9% | $0.03 | 73 | ||
| GPT-5.5 | 89.3% | $0.06 | 87 | ||
| Claude Fable 5.1 | answered pages only | 89.2% | $0.18 | 433 | |
| | 89% | $0.02 | 250 | ||
| Claude Sonnet 5 | 88.4% | $0.04 | 284 | ||
| Claude Fable 5.1 | 86.5% | $0.18 | 433 | ||
| Claude Opus 5.5 | 85.3% | $0.07 | 241 | ||
| | 80.9% | $0.003 | 93 | ||
| GPT-5.4 Nano | 76.1% | $0.002 | 73 | ||
| | 75.2% | $0.003 | 465 | ||
| Claude Haiku 4.5 | 72.8% | $0.01 | 284 | ||
| gpt-oss-20b | 71% | $0.0002 | 38 | ||
| | 65.7% | $0.0006 | 172 | ||
| | 59.7% | $0.0002 | 70 | ||
| | 23.1% | $0.0006 | 57 |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Feb 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
Versions
More in Safety and alignment
- FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
- StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is.
Inkling 98.6%
- AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
- MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy.
Muse Spark Open 98.1%
- Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. GPT-6 Astra 8.50%
- Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%