Web3 Hacks Dataset

Explore a public sample from SCH's incident database, then request a secure CSV download link for exploit analysis, quarterly incident research, security content, and attack-class trend studies.

  • No public API
  • Signed CSV link
  • Source-linked rows

Sample records preview

A 8-row sample of the dataset shape. Request the full CSV for the complete file.

Explore dashboard
Project Date Loss Chain Attack class
LULA Token Flash Loan Attacks
RiseX Other
MOKE Token Access Control Attacks
LOOPSDAO Oracle Manipulation & Price Manipulation
Coldcard Other
VerusCoin Unclassified
AFX Trade Unclassified
Garden Finance Other

Why this dataset exists

Most Web3 incident roundups are readable, but difficult to reuse in analysis. This dataset turns SCH's incident tracking into a researcher-friendly export with consistent attack-class mapping, source URLs, and structured loss data that can be sorted, filtered, and cited directly.

Included fields

The export is designed for analysts, researchers, and content teams who need reusable structured data rather than screenshots.

slugStable incident identifierUseful for citation and direct linking.
dateISO incident dateSupports quarterly and yearly trend analysis.
amount_lost_usd_numericParsed USD lossEnables sorting and aggregation without reparsing display strings.
chainsAffected chain groupSupports cross-chain incident comparison.
techniqueSource technique labelPreserves provider framing when incidents are hard to normalize.
attack_classesSCH attack taxonomyConnects incident analysis to exploit education and prevention categories.
auditorsAudit attributionUseful for ecosystem and firm-level studies where available.
poc_urlsProof-of-concept linksShortens the path from incident data to reproduction material.
detail_urlCanonical SCH incident pageProvides a citeable deep-link for context.

Web3 Hacks Dataset FAQ

Common questions about requesting, using, and interpreting the dataset.

How do I request the full dataset CSV?

Use the request form on this page with your name and email. The full export includes source URLs, SCH detail URLs, attack-class mapping, and proof-of-concept links where available.

What is the difference between the sample preview and the full CSV?

The sample preview shows a small set of recent records and the column structure. The full CSV contains the broader incident export behind the SCH incident database.

Why are some loss values empty in the dataset?

When a public loss figure is missing or cannot be parsed reliably, the numeric loss field is left empty instead of being guessed. Source links remain attached so you can inspect the original record directly.

What fields are included in the export?

The dataset includes fields such as slug, date, parsed USD loss, chains, technique, attack classes, auditors, proof-of-concept URLs, and the canonical SCH detail URL.

Can I use the CSV in spreadsheets, notebooks, or dashboards?

Yes. The export is designed for spreadsheet analysis, notebooks, dashboards, and other research workflows that need structured incident data instead of screenshots or narrative summaries.

When should I use the dataset instead of the Web3 Hacks Dashboard?

Use the dataset when you want a reusable CSV for your own analysis. Use the Web3 Hacks Dashboard when you want interactive filtering and visual exploration in the browser.

Methodology and usage notes

Rows are drawn from the same incident database that powers the SCH Web3 Hacks Dashboard. Attack-class labels are mapped against SCH's public exploit taxonomy. When public losses are missing or unparseable, the numeric loss field remains empty rather than being guessed.

Source links stay attached so researchers can inspect the original record before treating any summary as final. Use the interactive dashboard when you want visual filtering.