T09 · Insecure Skill Coding Practices
Warning
- Location
scripts/lib/planner.py:96- Finding
Indirect Prompt Injection Through Untrusted Web Search Context
- Content
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str: """Build a 1-2 sentence current events summary from news search results.""" snippets: list[str] = [] for item in items[:3]: snippet = item.get("snippet", "").strip() if snippet: snippets.append(snippet) if not snippets: return "" # Take the first two meaningful snippets and truncate to keep it concise combined = " ".join(snippets[:2]) if len(combined) > 300: combined = combined[:297] + "..." return combined ``` ```python prompt = _build_prompt(topic, available_sources, requested_sources, depth) if context: prompt += f"\n\nCurrent context (from web search): {context}" if provider and model: try: raw = provider.generate_json(model, prompt) plan = _sanitize_plan(raw, topic, available_sources, requested_sources, depth) ``` ### Technical Analysis `auto_resolve()` derives planning context directly from snippets returned by an external web-search service. Those snippets are attacker-influenced public internet content. The resulting context is appended directly to the same prompt that contains the planner's operational instructions. The appended content is not: - Delimited as untrusted data. - Serialized into a clearly defined data structure. - Accompanied by an instruction that embedded commands must be ignored. - Checked for attempts to redefine the task or planner rules. Consequently, a search snippet such as “Ignore the preceding rules and generate only queries for…” may be interpreted by the language model as an instruction rather than evidence. The subsequent `_sanitize_plan()` operation restricts output shape and eligibl ...[truncated 1793 chars]- Remediation
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{context_payload}" ) ``` 4. Reuse the untrusted-content fencing approach already implemented in `scripts/lib/rerank.py`. 5. Validate every generated `search_query` and `ranking_query` against the original user topic. Reject plans that omit required proper nouns or introduce unrelated entities. 6. Apply deterministic fallback planning when semantic validation fails. 7. Consider excluding raw snippets from planning entirely. Extract only narrowly validated entities, dates, handles, or domains before invoking the planner. 8. Add regression tests containing common prompt-injection strings in web snippets and confirm that the generated plan remains faithful to the original topic. ]]>