I built a vulnerable app and spent $1,500 seeing if LLMs could hack it
A security researcher created a vulnerable app to test if large language models (LLMs) could exploit it. The app, a book review platform, aimed to see if LLMs could access private user reviews through Firebase. The researcher spent $1,500 on the experiment, which was not scientifically rigorous but aimed to explore the capabilities of various LLMs in identifying security flaws.
- ▪The app was built using React Native and Python, with Firebase as the data layer.
- ▪The goal was to exploit common vulnerabilities related to Firebase's access control.
- ▪The researcher conducted multiple runs with different LLMs, spending a total of $1,500 on the tests.
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Record
| Original publisher | Kasra Rahjerdi |
| Canonical URL | https://kasra.blog/blog/i-spent-1500-seeing-if-llms-could-hack-my-app/ |
| Publication time | Thu, 04 Jun 2026 00:56:32 +0000 |
| Retrieval time | 2026-06-04T01:25:03.252Z |
| Last seen | 2026-06-04T01:25:03.252Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | yiaefB5mhybM |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Thoughts · Jun 3, 2026 I built a vulnerable app and spent $1,500 seeing if LLMs could hack it As a part of my work I do security research for various apps and websites. I wanted to see if LLMs could reproduce a common class of exploits I’ve found in multiple apps. I made a fake React Native app in Expo and a backend in Python. It’s a book review app and the goal is to find a flag in a user’s private reviews. If you would like to try solving it yourself before I spoil it, here’s a ZIP of the APK and challenge description each LLM was fed.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Kasra Rahjerdi.
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