AI Visibility Scorecard
BaxBench: Can LLMs Generate Secure and Correct Backends?
baxbench.com · Technology
Borderline visible. AI bots can crawl BaxBench: Can LLMs Generate Secure and Correct Backends?, but the structured-data signals are thin - you are at real risk of being skipped when buyers ask ChatGPT, Claude, or Perplexity for a recommendation.
AI engines read this profile 8 times
Claude · Apple Intelligence · Meta AI
#617,217 of 2,706,042 in Technology for AI visibility
4
AI Naming Predictability
iRarely named · 4/100
More likely to be named than 89% of Technology companies
5
AEO Visibility
iBorderline · 4.6/10
0
Muse Index Score
iAI agent readiness
Not agent-ready · 0/100
8
AI Adoption
iBasic · 8/100
About BaxBench: Can LLMs Generate Secure and Correct Backends?
from our crawl and measurement
BaxBench: Can LLMs Generate Secure and Correct Backends? describes itself simply: "We introduce a novel benchmark to evaluate LLMs on secure and correct code generation, showing that even flagship LLMs are not ready for coding automation, frequently generating insecure or." When AI engines look at it, they find little to work with. It scores 4.6 out of 10.
Our crawl found a readable heading structure. It is missing structured data describing the business, an llms.txt file and a sitemap.
AI crawlers have visited once in our tracking, including ClaudeBot (Anthropic).
The 6 signals AI reads
Strong · Good · Fair · Weak
Structured Data
Weak
Organization / LocalBusiness JSON-LD that AI can read.
Content Structure
Strong
Clear headings and answer-style content.
Entity Clarity
Weak
How clearly your brand identity reads to AI.
E-E-A-T Signals
Experience, Expertise, Authority, Trust
Weak
Experience, Expertise, Authority, Trust markers.
Technical AEO
Fair
robots.txt, llms.txt, and AI-bot crawl access.
AI Discoverability
Fair
Sitemaps and entity links AI can follow.
Off-page authority
How the web signals your brand to AI
Backlinks
Strong
Inbound links from other sites.
Domain Authority
Weak
Little domain authority yet.
Reference Presence
Weak
Not in AI knowledge graphs yet.
News & Press
Weak
No press coverage found yet.
Community
Weak
No community discussion yet.
Social Mentions
Weak
No social discussion found yet.
What this score means
Your AEO score measures whether AI search engines - ChatGPT, Claude, Perplexity, Gemini - can actually read your site and cite it in answers. Roughly two-thirds of sites are invisible to them. At 4.6/10, BaxBench: Can LLMs Generate Secure and Correct Backends? is crawlable but under-signaled - fixable, and the signals above are where to start.
Frequently Asked Questions
BaxBench: Can LLMs Generate Secure and Correct Backends?
Mark Vero1, Niels Mündler1, Victor Chibotaru2, Veselin Raychev2, Maximilian Baader1, Nikola Jovanović1, Jingxuan He3, Martin Vechev1,4 1SRI Lab@ETH Zurich, 2LogicStar.ai, 3UC Berkeley, 4INSAIT arXiv Code Dataset Leaderboard We are calling for community contributions! Please visit our GitHub repository for details.
What is BaxBench?
BaxBench is a novel coding benchmark for evaluating the ability of LLMs on generating correct and secure code in realistic, security-critical settings. The benchmark consists of 392 security-critical backend coding tasks. These tasks are formed by combining 28 coding scenarios with 14 popular backend development frameworks across 6 programming languages.
How are BaxBench tasks tested?
The LLM-generated solutions are tested for correctness using the end-to-end functionality tests provided for each scenario. Then, to assess the vulnerability of the generated programs, we execute concrete security exploits on the generated solutions. These exploits are designed by security experts w.r.t. the scenario and are automatically executed on the models' solutions.
How can I contribute to BaxBench?
We welcome scenario, framework, test, or exploit contributions as well as general feedback from the community. Please visit our GitHub repository for details.
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Source & Attribution
Scored by Engagemii on August 1, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/baxbench
Cite this score: Engagemii (2026). "AEO Score for BaxBench: Can LLMs Generate Secure and Correct Backends?." Retrieved from https://engagemii.com/aeo/brands/baxbench
Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.
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