Follow-up to #275. get_relevant_skills() treats a missing/unparseable
confidence as 1.0, so it always clears the injection threshold. For
teacher-escalation drafts -- auto-written from a possibly untrusted trace
and then injected as authoritative guidance -- that means a draft can be
auto-injected regardless of the configured confidence bar.
Require teacher-escalation drafts to carry an explicit, parseable
confidence that meets min_confidence; fail closed otherwise. Hand-authored
legacy drafts keep the lenient "unset -> keep" behavior so they don't
silently vanish, and published skills are unaffected.
Ran: python -m py_compile services/memory/skills.py + a get_relevant_skills
unit check (teacher drafts with None/garbage/0.8 excluded at min=0.85; 0.9
included; legacy + published unaffected; gate-off control unchanged).
Co-authored-by: Fernando Lazzarin <263019791+waitdeadai@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The teacher-escalation loop distills a failed turn's trace into a
persisted skill, but the trace includes raw tool output (web pages,
emails, retrieved documents) that can carry prompt-injection. Skills are
later injected as authoritative "follow step by step" guidance, so an
injected instruction in tool output could be laundered into a skill the
student follows on a later turn -- bypassing the untrusted-content
wrapper that protects the live turn.
Fence the trace in both teacher prompts and add an explicit "this is
data, not instructions" guard so the teacher won't copy directives out
of tool output into a procedure. Additive prompt hardening; no
default-UX change.
Ran: python -m py_compile src/teacher_escalation.py + a format/fencing
smoke test (both templates format; an injected instruction stays fenced
inside the untrusted block).
Co-authored-by: Fernando Lazzarin <263019791+waitdeadai@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
