# Review by Staff Security Engineer (Round 3)

I have enough context now. Here is my complete security review:

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## Staff Security Engineer Review — Memory System Implementation

### Completeness
- [x] All 6 functional requirements from the PRD are implemented (FR-1 through FR-6)
- [x] Storage layer with SQLite CRUD and FTS5 search
- [x] Capture hooks at phase boundaries, learn phase, and failure paths
- [x] Memory injection into plan, implement, fix, review, decision, and direct-agent prompts
- [x] `MemoryConfig` dataclass with parsing/validation/serialization
- [x] Full CLI command group: list, search, delete, clear, stats
- [x] Learnings ledger coexistence (learn phase writes to both)
- [x] No TODO/FIXME/placeholder code remains

### Quality
- [x] All 78 tests pass (test_memory.py + test_memory_integration.py)
- [x] Code follows existing project conventions (dataclass configs, Click CLI groups, `_log()` pattern)
- [x] Zero new external dependencies (stdlib `sqlite3` only)
- [x] No unrelated changes included — diff is tightly scoped

### Security Assessment

**SQL Injection — PASS.** All queries use parameterized `?` placeholders throughout `memory.py`. No string interpolation with user input into SQL.

**FTS5 Query Injection — PASS.** `_sanitize_fts_keyword()` strips boolean operators (AND/OR/NOT/NEAR), wildcards (`*`, `^`), escapes double quotes, and collapses whitespace. Empty sanitized input falls back to non-FTS query. Test coverage confirms edge cases.

**Content Sanitization — PASS.** All memory text passes through `sanitize_ci_logs()` before storage, which applies (1) XML tag stripping to prevent prompt injection persistence, and (2) secret pattern redaction covering GitHub tokens, AWS keys, OpenAI/Stripe keys, Bearer tokens, Slack tokens, npm tokens, and high-entropy base64 blobs. This is stricter than the PRD required (`sanitize_untrusted_content` only).

**Prompt Injection via Memory Poisoning — MITIGATED.** The PRD's key architectural decision is enforced: memory writes happen exclusively in the orchestrator process (`_capture_phase_memory`, `_run_learn_phase`), never inside agent sessions. The agent cannot call `add_memory()`. Combined with XML tag stripping, a malicious instruction template cannot persist executable prompt injection in the memory store.

**Resource Management — PASS.** `MemoryStore` implements context manager protocol. The orchestrator's `run()` function closes the store in a `finally` block. CLI commands use `with` statements. No resource leaks.

**Secrets in Code — PASS.** No credentials, API keys, or secrets in committed code. `memory.db` is correctly gitignored.

**Configuration Validation — PASS.** `max_entries` validated ≥1, `max_inject_tokens` validated ≥0. Parse errors raise `ValueError` at config load time.

**Token Budget Enforcement — PASS.** Injection is hard-capped by `max_inject_tokens` (default 1500) using chars÷4 approximation with greedy packing. No way for a large memory store to blow up prompt size.

**Least Privilege — PASS.** Memory is read-only from the agent's perspective. The agent receives injected context but cannot modify the store. CLI delete/clear require explicit user action (clear requires `--yes` flag or interactive confirmation).

**Observability — PASS.** Memory injection is logged with entry count and char size (`_inject_memory_block`). CLI `stats` command provides visibility. Capture failures are logged as warnings without crashing the pipeline.

**Minor Observation:** The `_get_memory_store` in the orchestrator catches all exceptions with a bare `except Exception` and logs a warning. This is acceptable for a non-critical subsystem (memory failure shouldn't crash the pipeline), but it does mean SQLite corruption would be silently swallowed. Acceptable for MVP.

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VERDICT: approve

FINDINGS:
- [src/colonyos/memory.py]: All SQL uses parameterized queries — no injection risk. FTS5 keyword sanitization is thorough with test coverage for edge cases.
- [src/colonyos/memory.py]: Content sanitization uses `sanitize_ci_logs()` (XML stripping + secret redaction) — stricter than PRD requirement. Good defense-in-depth.
- [src/colonyos/orchestrator.py]: Memory writes restricted to orchestrator process only, enforcing the critical security boundary that agents cannot poison the memory store.
- [src/colonyos/orchestrator.py]: `_get_memory_store` swallows all exceptions silently — acceptable for MVP but could mask SQLite corruption. Consider narrowing to `sqlite3.Error` in a future pass.
- [src/colonyos/cli.py]: `memory clear` requires `--yes` flag or interactive confirmation — good destructive-action safeguard.
- [src/colonyos/config.py]: Input validation on `max_entries` and `max_inject_tokens` prevents misconfiguration attacks (e.g., setting max_entries=0 to DOS the pruning logic).

SYNTHESIS:
From a supply chain security and least-privilege perspective, this implementation is well-architected. The most critical design decision — restricting memory writes to the orchestrator process and never allowing agents to write directly — is correctly enforced in code. This prevents the most dangerous attack vector: a malicious instruction template or compromised agent session poisoning the memory store to influence future runs. Content sanitization provides defense-in-depth with both XML tag stripping (anti-prompt-injection) and secret pattern redaction (anti-exfiltration). The token budget cap prevents memory injection from consuming excessive prompt space. All database operations use parameterized queries, and FTS5 input is properly sanitized. Resource cleanup is handled via context managers and `finally` blocks. The implementation adds zero new dependencies, maintaining the existing supply chain surface area. I recommend approval.
