{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/l-autoda-leveraging-large-language-models-for","title":"L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks","arxiv_id":"2401.15335","date":"2024-01-27","proceeding":null,"authors":["Ping Guo","Fei Liu","Xi Lin","Qingchuan Zhao","Qingfu Zhang"],"abstract":"In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require feedback on the decision of a model rather than detailed probabilities or scores, are particularly insidious and difficult to defend against. This work introduces L-AutoDA (Large Language Model-based Automated Decision-based Adversarial Attacks), a novel approach leveraging the generative capabilities of Large Language Models (LLMs) to automate the design of these attacks. By iteratively interacting with LLMs in an evolutionary framework, L-AutoDA automatically designs competitive attack algorithms efficiently without much human effort. We demonstrate the efficacy of L-AutoDA on CIFAR-10 dataset, showing significant improvements over baseline methods in both success rate and computational efficiency. Our findings underscore the potential of language models as tools for adversarial attack generation and highlight new avenues for the development of robust AI systems.","url_abs":"https://arxiv.org/abs/2401.15335v2","url_pdf":"https://arxiv.org/pdf/2401.15335v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"l-autoda-leveraging-large-language-models-for","repo_url":"https://github.com/pgg3/L-AutoDA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"l-autoda-leveraging-large-language-models-for","repo_url":"https://github.com/FeiLiu36/LLM4MOEA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2401.15335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15335"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/FeiLiu36/LLM4MOEA","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/pgg3/L-AutoDA","reach":null}],"summary":{"ran":5,"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1},"listed":{"samples":6,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"b04755c45bc26c0d","entry":"default_termination","repo":"FeiLiu36/LLM4MOEA","repo_kind":"listed","path":"MOEAD-LLM/pymoo/core/algorithm.py","file_url":"https://github.com/FeiLiu36/LLM4MOEA/blob/HEAD/MOEAD-LLM/pymoo/core/algorithm.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b04755c45bc26c0d"}},{"code_sha256_prefix":"e219d8aa93031c0a","entry":"draw_proposals","repo":"pgg3/L-AutoDA","repo_kind":"official","path":"core/attacks/AutoDA.py","file_url":"https://github.com/pgg3/L-AutoDA/blob/HEAD/core/attacks/AutoDA.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e219d8aa93031c0a"}},{"code_sha256_prefix":"dbd4210d0d367d05","entry":"find_outliers_upper_tail","repo":"FeiLiu36/LLM4MOEA","repo_kind":"listed","path":"MOEAD-LLM/pymoo/core/decision_making.py","file_url":"https://github.com/FeiLiu36/LLM4MOEA/blob/HEAD/MOEAD-LLM/pymoo/core/decision_making.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dbd4210d0d367d05"}},{"code_sha256_prefix":"24a2a3d5930291d1","entry":"plot_sphere","repo":"FeiLiu36/LLM4MOEA","repo_kind":"listed","path":"MOEAD-LLM/problem_interface.py","file_url":"https://github.com/FeiLiu36/LLM4MOEA/blob/HEAD/MOEAD-LLM/problem_interface.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"24a2a3d5930291d1"}},{"code_sha256_prefix":"3c81f0994213bd5b","entry":"stats_avg_nevals","repo":"FeiLiu36/LLM4MOEA","repo_kind":"listed","path":"MOEAD-LLM/pymoo/algorithms/hyperparameters.py","file_url":"https://github.com/FeiLiu36/LLM4MOEA/blob/HEAD/MOEAD-LLM/pymoo/algorithms/hyperparameters.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3c81f0994213bd5b"}},{"code_sha256_prefix":"0299b2bbd2a03fc2","entry":"stats_single_objective_mean","repo":"FeiLiu36/LLM4MOEA","repo_kind":"listed","path":"MOEAD-LLM/pymoo/algorithms/hyperparameters.py","file_url":"https://github.com/FeiLiu36/LLM4MOEA/blob/HEAD/MOEAD-LLM/pymoo/algorithms/hyperparameters.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0299b2bbd2a03fc2"}},{"code_sha256_prefix":"a7040e3e99b92e1b","entry":"create","repo":"FeiLiu36/LLM4MOEA","repo_kind":"listed","path":"MOEAD-LLM/pymoo/algorithms/hyperparameters.py","file_url":"https://github.com/FeiLiu36/LLM4MOEA/blob/HEAD/MOEAD-LLM/pymoo/algorithms/hyperparameters.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a7040e3e99b92e1b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}