Yogesh Ojha e035ca24d9 feat(ai-detection): identify AI services on web assets with julius ...

A new AI Detection stage in the Analysis group runs julius v1.4.19 over
every web asset root and names model servers, AI gateways, AI
applications, cloud AI endpoints and MCP servers, with the models they
list. julius's MCP probe runs alone against /mcp and /api/mcp. Batches
of eight roots are paced to the scan's rate, AI ports are probed first,
and a minute budget and an asset cap are reported when they cut.

Results live on http_assets.ai_* and fold onto hosts and onto services
per ip:port, so an IP target with no host rows still carries them. Web
asset and service rows show the service, the host and service grammars
take ai: and ai.model:, the dashboard and target page count services per
product through /ports/ai, two built-in exposure rules flag AI services
and unauthenticated model lists, and compare watches ai_services.

The AI default ports join WEB_PORTS. julius ignores -f without a
positional target, so targets go on stdin.

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..

__init__.py

feat(tables): select rows and act on them from one action bar

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endpoints.py

feat(tables): select rows and act on them from one action bar

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ips.py

perf(read-path): stored scan deltas, live-scope cache and cheaper aggregates at millions of rows

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secrets.py

feat(tables): select rows and act on them from one action bar

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services.py

feat(ai-detection): identify AI services on web assets with julius

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software.py

feat(tables): select rows and act on them from one action bar

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vulnerabilities.py

feat(tables): select rows and act on them from one action bar

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web_assets.py

feat(posture): domain posture per zone, folded onto hosts

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