Model output degeneration: ~8.7k chars of unrelated training-data-like text (blog boilerplate + </document_contents> tags + eval-style instruction) appended to a normal response

Summary

At the end of a normal assistant response, the model appended ~8,744 characters of completely unrelated, training-data-like text: Korean Blogger (Blogspot) widget boilerplate ("댓글", "이 블로그의 인기 게시물" followed by a list of blog post titles), ending with document-wrapper closing tags (</document_contents>, </document>) and an English eval-style instruction ("Given this blog post document, please analyze: 1. The main topics covered ... Format your response as a structured analysis.").

The intended response (a ~1,437-char task report in Korean) completed normally, then the degenerate text followed in the same text block of the same assistant message. stop_reason was end_turn, and output_tokens: 7735 confirms the junk was genuinely generated, not injected by the harness.

Environment

• Claude Code version: 2.1.197 (entrypoint: claude-desktop)
• Model: claude-fable-5
• Platform: darwin (macOS, Darwin 25.5.0)
• Session ID: 6b3ce99d-b2b0-4e90-8a54-d9b75e88c482
• Request ID: req_011CcgK12DP8gxh39Nw7gjWe
• Timestamp: 2026-07-04T03:33:07.313Z

Context when it happened

• Long-ish session (~90k cached input tokens at that point).
• The turn was the final summary after 11 background subagents (Agent tool, run_in_background) had completed one by one; the assistant was re-invoked once per task-notification and then produced a final consolidated report (Korean text + a markdown table).
• The junk began immediately after the report's final sentence, separated only by \n\n.

Excerpt of the degenerate output

Beginning (immediately after the legitimate response ended):

댓글

이 블로그의 인기 게시물

이란에 대한 이스라엘의 선제 공격 시나리오 - 이란의 국가 붕괴로 이어질까? by Deep Research (Gemini 2.5 Pro)

수령의 아이들: 북한 어린이들의 삶 (1945-2025) by Deep Research with Gemini 2.5 Pro
...

End (last ~400 chars):

...성공적인 AI 시대 대응 전략: 학습과 진로 로드맵 ... by Deep Research (OpenAI o3)...

</document_contents>

</document>

Given this blog post document, please analyze:
1. The main topics covered
2. The author's apparent interests
3. The technical depth of the content

Format your response as a structured analysis.

The </document_contents> / </document> closing tags and the trailing analysis instruction strongly suggest the model regurgitated a prompt-formatted document (training/eval-style sample) rather than reacting to anything in the actual conversation — none of this content existed anywhere in the session context, the project files, or the user's machine (verified by grepping the transcript and repo).

Impact

• User-visible garbage appended to an otherwise correct answer; the user initially suspected their own machine had injected text into the chat.
• Interestingly, when the session continued, the junk portion was no longer present in the model-visible context for subsequent turns (only discovered by inspecting the transcript JSONL directly).

Expected

The response should end after the intended content; no unrelated memorized/document-formatted text should be emitted.