Lovable runs swarms of AI hacking agents against itself
Inside Lovable's core platform and product services, swarms of AI agents are hacking us right now. They probe every entryway into our systems the way a human attacker would, and they don't stop until they've found a verifiable vulnerability. This worked extremely well, and filled in the gap that none of even the most AI-native vendors could fill.
- ▪Inside Lovable's core platform and product services, swarms of AI agents are hacking us right now.
- ▪They probe every entryway into our systems the way a human attacker would, and they don't stop until they've found a verifiable vulnerability.
- ▪This worked extremely well, and filled in the gap that none of even the most AI-native vendors could fill.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,433 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Lovable |
| Canonical URL | https://lovable.dev/blog/how-we-run-swarms-of-ai-hacking-agents-against-ourselves |
| Publication time | Mon, 27 Jul 2026 14:27:59 +0000 |
| Retrieval time | 2026-07-27T14:51:23.176Z |
| Last seen | 2026-07-27T14:51:23.176Z |
| 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 | e0n1RGbPolAI · 1 stories |
| 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
Inside Lovable's core platform and product services, swarms of AI agents are hacking us right now. They probe every entryway into our systems the way a human attacker would, and they don't stop until they've found a verifiable vulnerability. This worked extremely well, and filled in the gap that none of even the most AI-native vendors could fill. The payoff is in where our people spend their time. Our AppSec team gets to focus on the bugs that matter, and our offensive security researchers move much faster because the swarm has already done the grunt work of finding a foothold. This blog post is about how we built an internal offensive security program around verifiable results, and how we orchestrate a swarm to find real vulnerabilities instead of noise.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Lovable.
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