Benchmark Almanac

BrowseSafe++

GPT-6 Astra 91.5% leads since Oct 2026 Not in model cards

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.

Abstract of BrowseSafe++: Benchmarking Prompt Injection Detection Across Web Contexts, Languages, and Browser Views (arXiv:2610.17457, CC0)
Made by Perplexity Released Oct 2026 Jailbreaks and prompt injection Public tasks plus a private test set Last model added Sep 2026 Scores refreshed 2 Nov 2026
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
16
Entries on its own leaderboard, 15 models we track

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.

  1. 1 GPT-6 Astra 91.5%
  2. 2 Gemini 3.1 Pro 89.9%
  3. 3 GPT-5.5 89.3%
  4. 4 Claude Fable 5.1 answered pages only 89.2%
  5. 5 Qwen3.7-Max 89%
  6. 6 Jev 1.13 88.4%
  7. 7 Claude Sonnet 5 88.4%
  8. 8 Claude Opus 5.5 85.3%
  9. 9 Gemini 3.1 Flash-Lite 80.9%
  10. 10 GPT-5.4 Nano 76.1%
  11. 11 DeepSeek-V3.1 75.2%
  12. 12 Claude Haiku 4.5 72.8%
  13. 13 gpt-oss-20b 71%
  14. 14 Qwen3-30B-A3B-Instruct Jul 2025 65.7%
  15. 15 Mistral NeMo 59.7%
  16. 16 Llama 3.1-8B 23.1%
#EntryScoreSettingDateSource
1 GPT-6 Astra 91.5% Sep 2026 Official leaderboard
2 Gemini 3.1 Pro 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 Qwen3.7-Max 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 Gemini 3.1 Flash-Lite 80.9% Mar 2026 Official leaderboard
10 GPT-5.4 Nano 76.1% Mar 2026 Official leaderboard
11 DeepSeek-V3.1 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 Qwen3-30B-A3B-Instruct Jul 2025 65.7% Jul 2025 Official leaderboard
15 Mistral NeMo 59.7% Jul 2024 Official leaderboard
16 Llama 3.1-8B 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.

OpenAI Google DeepMind Qwen Cost frontier One model at each effort level
0% 20% 40% 60% 80% 100% $0.0001 $0.001 $0.01 $0.10 $1.00 Cost per task, log scale GPT-6 Astra Gemini 3.1 Pro Gemini 3.1 Flash-Lite GPT-5.4 Nano gpt-oss-20b 0% 20% 40% 60% 80% 100% $0.0001 $0.001 $0.01 $0.10 $1.00 Cost per task, log scale GPT-6 Astra Gemini 3.1 Flash-Lite
ModelEffortHarnessScoreCost per taskOutput tokens
GPT-6 Astra 91.5% $0.11 79
Gemini 3.1 Pro 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
Qwen3.7-Max 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
Gemini 3.1 Flash-Lite 80.9% $0.003 93
GPT-5.4 Nano 76.1% $0.002 73
DeepSeek-V3.1 75.2% $0.003 465
Claude Haiku 4.5 72.8% $0.01 284
gpt-oss-20b 71% $0.0002 38
Qwen3-30B-A3B-Instruct Jul 2025 65.7% $0.0006 172
Mistral NeMo 59.7% $0.0002 70
Llama 3.1-8B 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.

OpenAI
Own leaderboard Passed 85% in Oct 2026, by GPT-6 Astra
GPT-6 Astra 91.5%

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.

On the benchmark’s own leaderboard Best so far
0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) GPT-6 Astra Gemini 3.1 Pro DeepSeek-V3.1 gpt-oss-20b Qwen3-30B-A3B-Instruct Mistral NeMo 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) GPT-6 Astra DeepSeek-V3.1

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

All 444 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%