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fix(deep-research): wrap fetched webpage content in untrusted-context sandbox

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The goal-based extractor passed raw fetched webpage content straight
into the LLM prompt via string substitution, bypassing the
prompt-injection hardening layer in src/prompt_security.py.

Split EXTRACTOR_PROMPT into EXTRACTOR_SYSTEM (task instructions +
goal, trusted) and a second message built with untrusted_context_message()
(raw page content, sandboxed with <<<UNTRUSTED_SOURCE_DATA>>> guards).
This aligns the extractor with every other external-content injection
site in the codebase (agent_loop, chat_processor, chat_routes).

Fixes #3044

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Giuseppe
2026-06-06 11:37:10 +02:00
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parent 893cb8254f
commit e87a1ad8d2
2 changed files with 14 additions and 16 deletions
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src/goal_based_extractor.py
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@@ -3,22 +3,18 @@
Goal-based content extraction prompt inspired by Alibaba Tongyi DeepResearch.
"""
EXTRACTOR_PROMPT = """Please process the following webpage content and user goal to extract relevant information:
EXTRACTOR_SYSTEM = """Extract relevant information from a webpage for a given research goal.
## **Webpage Content**
{webpage_content}
Goal: {goal}
## **User Goal**
{goal}
Task guidelines:
1. Locate the specific sections directly related to the goal within the provided webpage content.
2. Identify and extract the most relevant information; output full original context where possible, up to three or more paragraphs.
3. Organize into a concise paragraph with logical flow, judging each piece of information's contribution to the goal.
## **Task Guidelines**
1. **Content Scanning for Rational**: Locate the **specific sections/data** directly related to the user's goal within the webpage content
2. **Key Extraction for Evidence**: Identify and extract the **most relevant information** from the content, you never miss any important information, output the **full original context** of the content as far as possible, it can be more than three paragraphs.
3. **Summary Output for Summary**: Organize into a concise paragraph with logical flow, prioritizing clarity and judge the contribution of the information to the goal.
Respond in JSON with exactly these fields: "rational", "evidence", "summary".
**Final Output Format using JSON format has "rational", "evidence", "summary" fields**
Example output:
Example:
{{
"rational": "This section discusses X which directly relates to the goal of understanding Y",
"evidence": "Full quotes and context from the page...",