Authenticate interaction provenance and remote approvals
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
## Parent and milestone
- Parent issue: #43
- Milestone: not scheduled; implementation remains gated by #15.
## Problem
PTY output and repository content are untrusted text. They can imitate an
approval prompt, while a remote user may be unable to distinguish a real
runtime interaction from prompt injection or replayed output.
## Outcome
Define authenticated interaction provenance and enforcement rules that bind
every decision to the current fenced run, source, action, sequence, and expiry.
## Scope
- Record runtime, node, run, lease, sequence, action payload hash, source type,
confidence, expiry, and idempotency identity.
- Distinguish structured runtime interactions from degraded PTY heuristics.
- Define which risk classes can never be approved from heuristic text alone.
- Bind Telegram and future-client callbacks to one authorized interaction.
- Preserve exact action context for audit while applying redaction policy.
## Non-goals
- Replacing Alfred's decision policy.
- Treating a displayed message or Telegram callback token as proof of runtime
origin by itself.
## Acceptance criteria
- [ ] Replay, stale lease, duplicate callback, forged prompt, and source
downgrade fail conservatively.
- [ ] High-risk heuristic interactions cannot become executable approvals.
- [ ] UI/view models display source and degraded state without vendor types.
- [ ] Decision and interaction audit records are immutable and correlated.
- [ ] Structured and PTY regression scenarios are specified.
## Validation
- Adversarial prompt-injection, replay, reordering, and stale-owner tests.
## Dependencies
- #13 interaction audit, #26 normalized runtime events, #41 control API, and
Alfred's future policy contract.
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reading the dependency issues #13, #26, and #41, then trace the interaction audit, normalized runtime events, control API, Telegram callbacks, and UI/view-model boundaries described here. Define the provenance and enforcement contract before implementation; done means the listed replay, forgery, downgrade, audit-correlation, and structured-versus-PTY regression scenarios are specified and pass conservatively.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- authentication, backend-api-design, distributed-systems, security, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100