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Project

OSS Scanner

Anthropic's free, opt-in service that sends model-generated vulnerability reports to eligible open-source projects.

Known aliases

  • Anthropic OSS Scanner

Current stories

buildOne report1 publisher

Anthropic's Cyber Mission targets the months between finding a bug and fixing it

Anthropic's Cyber Mission puts Claude models, engineers and funding behind bug fixing, after Glasswing finds often waited months for a patch. Its free OSS Scanner sends unreviewed reports that Anthropic expects to be over 90% accurate, so it suits maintainers who have time to verify them.

Publishers:dev.to

Reality

Evidence40
Adoption20
Hype gap+10
Incentives55
Confidence40
securityConfirmed9 publishers

Anthropic pairs Claude with CrowdStrike and Dragos in a critical infrastructure defense program

Anthropic announced a Critical Infrastructure Defense Program that pairs Claude with outside security firms, CrowdStrike among them. Alongside it, Anthropic is running a free, opt-in scanning service for open-source maintainers directly, CyberScoop reported.

Perspective Coverage

9 publishers
Builder
Builder 42%
Operator
Operator 38%
Investor
Investor 20%

Reality

Evidence66
Adoption38
Hype gap+12
Incentives68
Confidence64
securityConfirmed6 publishers

Anthropic sends unreviewed AI bug reports with proofs of concept to open source maintainers

Anthropic expects more than 90% of the unreviewed vulnerability reports its new OSS Scanner sends to open source maintainers to be real. Opt-in projects get findings faster and take on the work of catching the errors, such as wrong severity ratings.

Perspective Coverage

6 publishers
Builder
Builder 52%
Operator
Operator 37%
Investor
Investor 11%

Reality

Evidence60
Adoption35
Hype gap+15
Incentives70
Confidence62
investConfirmed2 publishers

Nethermind and ZEUS seek Anthropic AI bug reports that skip human review

Nethermind and ZEUS applied for Anthropic's OSS Scanner a day after it launched, asking for AI scans of Ethereum client and Bitcoin wallet code. Anthropic had manually checked about 6,000 of the 29,000-plus flaws its models flagged, crypto.news reported, so the teams that get the reports have to judge them.

Reality

Evidence55
Adoption12
Hype gap+20
Incentives50
Confidence60