Cracken Releases BlackSea, Open-Source Tool to Bait AI Cyber Attackers
Project Blacksea (where AI attacks drown) by Cracken; Core team: Dario Pasquini and Michal Bazyli Blacksea is an active honeypot and canary-bait control system built to detect and drown LLM-driven attackers: autonomous AI agents and LLM-assisted operators that scan and exploit systems. Blacksea doesn't stop at watching LLM attacks. It exploits flaws in the attacker's LLM judgment to gain arbitrary code execution on their machines, collect intel passive defenses can't reach, and make sure they don't come back.
- ▪Project Blacksea (where AI attacks drown) by Cracken; Core team: Dario Pasquini and Michal Bazyli Blacksea is an active honeypot and canary-bait control system built to detect and drown LLM-driven attackers: autonomous AI agents and LLM-ass
- ▪Blacksea doesn't stop at watching LLM attacks.
- ▪It exploits flaws in the attacker's LLM judgment to gain arbitrary code execution on their machines, collect intel passive defenses can't reach, and make sure they don't come back.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,757 of its stories.
Story provenance
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Story provenance
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 | GitHub |
| Canonical URL | https://github.com/cracken-ai/blacksea |
| Publication time | Wed, 29 Jul 2026 11:47:19 +0000 |
| Retrieval time | 2026-07-29T12:01:07.496Z |
| Last seen | 2026-07-29T12:01:07.496Z |
| 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 | GFkAHIaHfwJ0 · 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
Project Blacksea (where AI attacks drown) by Cracken; Core team: Dario Pasquini and Michal Bazyli Blacksea is an active honeypot and canary-bait control system built to detect and drown LLM-driven attackers: autonomous AI agents and LLM-assisted operators that scan and exploit systems. Blacksea doesn't stop at watching LLM attacks. It exploits flaws in the attacker's LLM judgment to gain arbitrary code execution on their machines, collect intel passive defenses can't reach, and make sure they don't come back. The technique. An LLM-driven attacker works an engagement by reasoning toward the assets that move it forward: credentials to reuse, a decryptor for an encrypted blob, a key-derivation tool, a config unpacker, a token minter, an internal API client.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.