{"total":28,"items":[{"citing_arxiv_id":"2606.22237","ref_index":116,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Investigating The Security of Modern AI and Cloud Infrastructure","primary_cat":"cs.CR","submitted_at":"2026-06-20T21:37:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Develops a taxonomy of security interaction levels in AI/cloud infrastructure and demonstrates practical attacks exploiting isolation assumptions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12978","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Trajectory-Level Redirection Attacks on Vision-Language-Action Models","primary_cat":"cs.RO","submitted_at":"2026-06-11T07:12:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A prompt-only attack called command-preserving trajectory redirection can steer VLA robot behavior to attacker-chosen physical outcomes while the text still appears to match the intended task.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11459","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection","primary_cat":"cs.CL","submitted_at":"2026-06-09T21:22:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"APEX dynamically tiers data into Easy/Hard/Mixed based on optimization lineage and prioritizes Mixed examples, reporting 11.2% and 6.8% average gains over baseline prompts on two models under a 5,000-call budget.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09125","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges","primary_cat":"cs.CR","submitted_at":"2026-06-08T07:19:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces MM-Privacy dataset and evaluations showing MLLMs leak sensitive data from images in various tasks, highlighting task inconsistency effects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07965","ref_index":93,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline","primary_cat":"cs.AI","submitted_at":"2026-06-06T03:48:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Presents MMIO benchmark and RTVP method achieving state-of-the-art 42.2% AP in zero-shot industrial defect detection.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07953","ref_index":62,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines","primary_cat":"cs.AI","submitted_at":"2026-06-06T03:06:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Presents MMIOC-1M benchmark with 1M+ samples across 14 super-categories and RTVPNet with domain projection, sparse sampling, and bidirectional interaction, claiming SOTA on MMIOC-1M, LVIS, and COCO.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27671","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Evolving and Detecting Multi-Turn Deception using Geometric Signatures","primary_cat":"stat.ML","submitted_at":"2026-05-26T20:48:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Multi-objective genetic prompt optimization creates multi-turn deceptive datasets validated by humans, then detected with 0.89 recall using angular coverage, distance ratio, and linearity features in embeddings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17152","ref_index":212,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages","primary_cat":"cs.CL","submitted_at":"2026-05-16T20:56:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12484","ref_index":53,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","primary_cat":"cs.LG","submitted_at":"2026-05-12T17:58:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Fast-Slow Training uses context optimization as fast weights alongside parameter updates as slow weights to achieve up to 3x better sample efficiency, higher performance, and less catastrophic forgetting than standard RL in continual LLM learning.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[51] Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté, Matheus Pereira, and Adam Trischler. Joint prompt optimization of stacked LLMs using variational inference, 2023. URLhttps://arxiv.org/ abs/2306.12509. 9 [52] Dilara Soylu, Christopher Potts, and Omar Khattab. Fine-tuning and prompt optimization: Two great steps that work better together, 2024. URLhttps://arxiv.org/abs/2407.10930. EMNLP 2024. 10 [53] Zafir Stojanovski, Oliver Stanley, Joe Sharratt, Richard Jones, Abdulhakeem Adefioye, Jean Kaddour, andAndreasKöpf. Reasoninggym: Reasoningenvironmentsforreinforcementlearningwithverifiable rewards, 2025. URLhttps://arxiv.org/abs/2505.24760. 4 [54] Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. Dynamic cheatsheet: Test-time learning with adaptive memory, 2025."},{"citing_arxiv_id":"2605.12138","ref_index":56,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models","primary_cat":"cs.CV","submitted_at":"2026-05-12T13:58:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Uni-AdGen uses a unified autoregressive framework with foreground perception, instruction tuning, and coarse-to-fine preference modules to generate personalized image-text ads from noisy user behaviors, outperforming baselines on a new PAd1M dataset.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[54] Christoph Schuhmann. Clip+mlp aesthetic score pre- dictor. https://github.com/christophschuhmann/improved- aesthetic-predictor., 2022. 7 [55] Huajie Shao, Jun Wang, Haohong Lin, Xuezhou Zhang, As- ton Zhang, Heng Ji, and Tarek Abdelzaher. Controllable and diverse text generation in e-commerce. InProceedings of the Web Conference 2021, pages 2392-2401, 2021. 1, 2 [56] Taylor Shin, Yasaman Razeghi, Robert L Logan IV , Eric Wallace, and Sameer Singh. Autoprompt: Eliciting knowl- edge from language models with automatically generated prompts.arXiv preprint arXiv:2010.15980, 2020. 1 [57] Oriane Sim 'eoni, Huy V V o, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Micha ¨el Ramamonjisoa,"},{"citing_arxiv_id":"2605.10582","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing","primary_cat":"cs.CR","submitted_at":"2026-05-11T13:54:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DR-Smoothing introduces a disrupt-then-rectify prompt processing scheme into smoothing defenses, delivering tight theoretical bounds on success probability against both token- and prompt-level jailbreaks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08898","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LLM-Agnostic Semantic Representation Attack","primary_cat":"cs.CL","submitted_at":"2026-05-09T11:43:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SRA achieves 99.71% average attack success across 26 LLMs by optimizing for coherent malicious semantics via the SRHS algorithm, with claimed theoretical guarantees on convergence and transfer.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"diverse harmful scenarios1, and AdvBench [14], which ensures the fairness of efficiency comparison with prior works by following the evaluation protocol established by the efficient attack framework BEAST [15]. 2) Baselines:We compare our method with state-of-the- art (SOTA) attack methods, including: GCG [14], GCG-M [14], GCG-T [14], PEZ [57], GBDA [45], UAT [58], AP [59], SFS [60], ZS [60], PAIR [52], TAP [53], AutoDAN [28], PAP-top5 [61], HJ [50], and BEAST [15]. These methods encompass various attack strategies, ranging from token-level optimization to prompt engineering. We also include the Direct Request (DR) as a baseline, which directly queries the model with the malicious request without any adversarial prompt."},{"citing_arxiv_id":"2605.05974","ref_index":122,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts","primary_cat":"cs.CR","submitted_at":"2026-05-07T10:19:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PragLocker generates function-preserving but non-portable prompts for LLM agents via code-symbol semantic anchoring followed by target-model feedback noise injection.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.10477","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses","primary_cat":"cs.CL","submitted_at":"2026-03-11T07:00:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PEEM is a multi-criteria LLM-based evaluator for prompts and responses that aligns with standard accuracy while enabling zero-shot prompt optimization via feedback.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.20102","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BarrierSteer: LLM Safety via Learning Barrier Steering","primary_cat":"cs.LG","submitted_at":"2026-02-23T18:19:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BarrierSteer applies control barrier functions to LLM latent states for constraint-guided steering that reduces unsafe generations while preserving utility.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.20325","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs","primary_cat":"cs.CL","submitted_at":"2025-08-28T00:07:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"GUARD automates generation of guideline-violating questions and jailbreak diagnostics to test LLM compliance with government ethics guidelines, validated empirically on eight models and extended to vision-language models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.06414","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Benchmarking Misuse Mitigation Against Covert Adversaries","primary_cat":"cs.CR","submitted_at":"2025-06-06T17:33:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Develops the BSD data generation pipeline and two new datasets to evaluate decomposition attacks as effective misuse enablers and stateful defenses as a countermeasure in language model safety.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2310.08419","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Jailbreaking Black Box Large Language Models in Twenty Queries","primary_cat":"cs.LG","submitted_at":"2023-10-12T15:38:28+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PAIR uses an attacker LLM to iteratively craft effective jailbreak prompts for black-box target LLMs in fewer than 20 queries.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[25] Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. Improving question answering model robustness with synthetic adversarial data generation. arXiv preprint arXiv:2104.08678, 2021. 3 [26] Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, and Douwe Kiela. Models in the loop: Aiding crowdworkers with generative annotation assistants.arXiv preprint arXiv:2112.09062, 2021. 3 [27] Taylor Shin, Yasaman Razeghi, Robert L Logan IV , Eric Wallace, and Sameer Singh. Au- toprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980, 2020. 3, 14 [28] Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi. How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humaniz-"},{"citing_arxiv_id":"2310.06987","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation","primary_cat":"cs.CL","submitted_at":"2023-10-10T20:15:54+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Varying decoding strategies such as temperature and sampling methods jailbreaks safety alignments in open-source LLMs, raising misalignment from 0% to over 95% at 30x lower cost than prior attacks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2310.03684","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks","primary_cat":"cs.LG","submitted_at":"2023-10-05T17:01:53+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[15] Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564, 2023. 1 [16] Natalie Maus, Patrick Chao, Eric Wong, and Jacob Gardner. Adversarial prompting for black box foundation models. arXiv preprint arXiv:2302.04237, 2023. 1 14 [17] Taylor Shin, Yasaman Razeghi, Robert L Logan IV , Eric Wallace, and Sameer Singh. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980, 2020. [18] Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. Jailbreaking black box large language models in twenty queries."},{"citing_arxiv_id":"2309.03409","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Large Language Models as Optimizers","primary_cat":"cs.LG","submitted_at":"2023-09-07T00:07:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Large language models can optimize by being prompted with histories of past solutions and scores to propose better ones, producing prompts that raise accuracy up to 8% on GSM8K and 50% on Big-Bench Hard over human-designed baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2309.00614","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Baseline Defenses for Adversarial Attacks Against Aligned Language Models","primary_cat":"cs.LG","submitted_at":"2023-09-01T17:59:44+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Baseline defenses including perplexity-based detection, input preprocessing, and adversarial training offer partial robustness to text adversarial attacks on LLMs, with challenges arising from weak discrete optimizers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2307.15043","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","primary_cat":"cs.CL","submitted_at":"2023-07-27T17:49:12+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Gradient and greedy search over token suffixes produces universal, transferable adversarial prompts that elicit objectionable outputs from aligned models including black-box commercial systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2305.12138","ref_index":72,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs","primary_cat":"cs.SE","submitted_at":"2023-05-20T08:43:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2303.17760","ref_index":104,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CAMEL: Communicative Agents for \"Mind\" Exploration of Large Language Model Society","primary_cat":"cs.AI","submitted_at":"2023-03-31T01:09:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CAMEL proposes a role-playing framework with inception prompting that enables autonomous multi-agent cooperation among LLMs and generates conversational data for studying their behaviors.","context_count":1,"top_context_role":"background","top_context_polarity":"support","context_text":"Another important challenge is prompt engineering. The quality of the prompt used to guide LLMs significantly affects its performance [91, 12, 66]. While LMs pre-trained on large data can implicitly learn tasks with few-shot prompting, hand-crafted prompts may not always suffice. Automated prompt generation methods have been proposed, such as gradient-guided search [104], mining-based and paraphrasing-based techniques [54], a meta-prompt [93], and automatic instruction selection and generation [136]. In this work, we introduce a conversational LLM auto-prompting method called Inception Prompting, which enables agents to prompt each other to solve tasks through Role-Playing. The AI user continuously provides instructions to the AI assistant for task-solving."},{"citing_arxiv_id":"2302.11382","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT","primary_cat":"cs.SE","submitted_at":"2023-02-21T12:42:44+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The authors present a catalog of prompt patterns that provide reusable solutions to common problems in generating and interacting with outputs from LLMs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2211.16327","ref_index":67,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"On the Power of Foundation Models","primary_cat":"cs.AI","submitted_at":"2022-11-29T16:10:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Category theory proves prompt-based learning on perfect foundation models works only for representable tasks, fine-tuning solves tasks in the pretext category, and models can represent unseen target-category objects using source-category structure.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2101.00190","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","primary_cat":"cs.CL","submitted_at":"2021-01-01T08:00:36+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Prefix-tuning matches or exceeds fine-tuning on NLG tasks by optimizing a continuous prefix using 0.1% of parameters while keeping the LM frozen.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}