# ChatGPT: Excellent Paper! Accept It. Editor: Imposter Found! Review Rejected

arXiv: https://arxiv.org/abs/2512.20405 · HTML: https://mlsanity.com/paper/2512.20405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 10 of 10)

- [Optimization-based Prompt Injection Attack to LLM-as-a-Judge](https://mlsanity.com/paper/2403.17710/) (2024)
- [The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections](https://mlsanity.com/paper/2510.09023/) (2025)
- [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024)
- [TopicAttack: An Indirect Prompt Injection Attack via Topic Transition](https://mlsanity.com/paper/2507.13686/) (2025)
- [Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review](https://mlsanity.com/paper/2507.06185/) (2025)
- [Prompt Injection Attacks on LLM Generated Reviews of Scientific Publications](https://mlsanity.com/paper/2509.10248/) (2025)
- [Investigating the Vulnerability of LLM-as-a-Judge Architectures to Prompt-Injection Attacks](https://mlsanity.com/paper/2505.13348/) (2025)
- ["Give a Positive Review Only": An Early Investigation Into In-Paper Prompt Injection Attacks and Defenses for AI Reviewers](https://mlsanity.com/paper/2511.01287/) (2025)
- [PhantomLint: Principled Detection of Hidden LLM Prompts in Structured Documents](https://mlsanity.com/paper/2508.17884/) (2025)
- [Prompt-in-Content Attacks: Exploiting Uploaded Inputs to Hijack LLM Behavior](https://mlsanity.com/paper/2508.19287/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) like ChatGPT are now widely used in writing and reviewing scientific papers. While this trend accelerates publication growth and reduces human workload, it also introduces serious risks. Papers written or reviewed by LLMs may lack real novelty, contain fabricated or biased results, or mislead downstream research that others depend on. Such issues can damage reputations, waste resources, and even endanger lives when flawed studies influence medical or safety-critical systems. This research explores both the offensive and defensive sides of this growing threat. On the attack side, we demonstrate how an author can inject hidden prompts inside a PDF that secretly guide or "jailbreak" LLM reviewers into giving overly positive feedback and biased acceptance. On the defense side, we propose an "inject-and-detect" strategy for editors, where invisible trigger prompts are embedded into papers; if a review repeats or reacts to these triggers, it reveals that the review was generated by an LLM, not a human. This method turns prompt injections from vulnerability into a verification tool. We outline our design, expected model behaviors, and ethical safeguards for deployment. The goal is to expose how fragile today's peer-review process becomes under LLM influence and how editorial awareness can help restore trust in scientific evaluation.