[For merge][part 2] Support Gedit Evaulate#161
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the Highlights
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Code Review
This pull request introduces support for evaluating the G-Edit and AIME2025 datasets, including new configurations, dataset loaders, and LLM-based evaluation logic. The changes are extensive, adding base classes for judge datasets and utilities for handling multimodal data and efficient file I/O. A critical security concern has been identified: a path traversal vulnerability in the image loading logic for multimodal judge datasets, which could allow arbitrary file reads. Additionally, potential prompt injection instances were found where untrusted model outputs or dataset instructions are directly embedded into prompts without proper sanitization. Beyond security, areas for improvement include fixing contradictory instructions in prompts, addressing potential runtime errors, optimizing dataset creation for better performance, and cleaning up code style.
| image_path = os.path.join(base_path, pred_item.get('prediction', '')) | ||
| if image_path and os.path.exists(image_path): | ||
| try: | ||
| # 加载图片 | ||
| with Image.open(image_path) as img: |
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The _load_from_predictions method constructs an image path by joining a base directory with a prediction string obtained from a model's output. If the model's output is manipulated (e.g., via prompt injection), an attacker can specify a path like ../../../../etc/passwd. While Image.open() might fail on non-image files, this still allows for file existence probing and potential arbitrary file read of any image-compatible files on the system. The content of the file is then base64-encoded and potentially exposed to the judge model.
| example['content'] = AIS_IMAGE_START + image_url + AIS_CONTENT_TAG \ | ||
| + AIS_TEXT_START + example['instruction'] + AIS_CONTENT_TAG |
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A prompt injection vulnerability exists here: the dataset instruction is directly concatenated into the content field using special tags like AIS_CONTENT_TAG. If the instruction itself contains these tags (e.g., <AIS_CONTENT_TAG>, <AIS_IMAGE_START>), it can break the prompt structure and inject arbitrary segments (text or images) into the final prompt processed by the format_mm method. This allows for prompt injection attacks via the dataset content. Additionally, the current approach of creating a new Dataset object for each example and merging them with concatenate_datasets is inefficient; consider processing examples into a list of dictionaries and creating a single Dataset at the end using Dataset.from_list() for better performance.
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| <Original Question Begin>: \n{question}\n<Original Question End>\n\n | ||
| <Gold Target Begin>: \n{answer}\n<Gold Target End>\n\n | ||
| <Predicted Answer Begin>: \n{model_answer}\n<Predicted End>\n\n |
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The model_answer is directly embedded into the GRADER_TEMPLATE. Since model_answer is the output of the model being evaluated, it is untrusted. A malicious or manipulated model could output content that breaks out of the intended block (e.g., by including <Predicted End>) and injects new instructions to the judge model, potentially biasing the evaluation results.
| Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. | ||
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The instruction on line 48, "Simply reply with either CORRECT, INCORRECT", contradicts the instructions on lines 46 and 55, which ask for "A" or "B". This inconsistency could confuse the LLM judge. Since the post-processor get_a_or_b expects 'A' or 'B', these lines should be removed for consistency.
| A score from 0 to 10 will be given based on the success of the editing. (0 indicates that the scene in the edited image does not follow the editing instruction at all. 10 indicates that the scene in the edited image follow the editing instruction text perfectly.) | ||
| A second score from 0 to 10 will rate the degree of overediting in the second image. (0 indicates that the scene in the edited image is completely different from the original. 10 indicates that the edited image can be recognized as a minimal edited yet effective version of original.) |
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There are a couple of grammatical errors in the prompt which could be corrected for clarity:
- On line 24, "follow" should be "follows".
- On line 25, "version of original" should be "version of the original".
| A score from 0 to 10 will be given based on the success of the editing. (0 indicates that the scene in the edited image does not follow the editing instruction at all. 10 indicates that the scene in the edited image follow the editing instruction text perfectly.) | |
| A second score from 0 to 10 will rate the degree of overediting in the second image. (0 indicates that the scene in the edited image is completely different from the original. 10 indicates that the edited image can be recognized as a minimal edited yet effective version of original.) | |
| A score from 0 to 10 will be given based on the success of the editing. (0 indicates that the scene in the edited image does not follow the editing instruction at all. 10 indicates that the scene in the edited image follows the editing instruction text perfectly.) | |
| A second score from 0 to 10 will rate the degree of overediting in the second image. (0 indicates that the scene in the edited image is completely different from the original. 10 indicates that the edited image can be recognized as a minimal edited yet effective version of the original.) |
| From scale 0 to 10: | ||
| A score from 0 to 10 will be given based on image naturalness. | ||
| ( | ||
| 0 indicates that the scene in the image does not look natural at all or give a unnatural feeling such as wrong sense of distance, or wrong shadow, or wrong lighting. |
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There is a grammatical error here: "give a unnatural" should be "gives an unnatural".
| 0 indicates that the scene in the image does not look natural at all or give a unnatural feeling such as wrong sense of distance, or wrong shadow, or wrong lighting. | |
| 0 indicates that the scene in the image does not look natural at all or gives an unnatural feeling such as wrong sense of distance, or wrong shadow, or wrong lighting. |
| if len(dataset_batches) == 1: | ||
| return dataset_batches[0] | ||
| else: | ||
| from datasets import concatenate_datasets |
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It's a best practice to place all imports at the top of the file. This improves readability and helps avoid potential issues like circular dependencies. Please move from datasets import concatenate_datasets to the top of the file.
| from PIL import Image | ||
| from tqdm import tqdm | ||
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| from ais_bench.benchmark.datasets.needlebench_v2 import origin |
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The import from ais_bench.benchmark.datasets.needlebench_v2 import origin appears to be unused in this file. It should be removed to maintain code cleanliness.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
| Convert PIL Image to base64 string | ||
| """ | ||
| if not isinstance(image, Image.Image): | ||
| raise ValueError("Input must be a PIL Image object") |
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【review】使用aisbench错误码维护
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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PR Type / PR类型
Related Issue | 关联 Issue
Fixes #(issue ID / issue 编号) / Relates to #(issue ID / issue 编号)
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