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Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety

Xingjun Ma, Yifeng Gao, Yixu Wang, Ruofan Wang, Xin Wang, Ye Sun, Yifan Ding, Hengyuan Xu, Yunhao Chen, Yunhan Zhao, Hanxun Huang, Yige Li, Yutao Wu, Jiaming Zhang, Xiang Zheng, Yang Bai, Zuxuan Wu, Xipeng Qiu, Jingfeng Zhang, Yiming Li, Xudong Han, Haonan Li, Jun Sun, Cong Wang, Jindong Gu, Baoyuan Wu, Siheng Chen, Tianwei Zhang, Yang Liu, Mingming Gong, Tongliang Liu, Shirui Pan, Cihang Xie, Tianyu Pang, Yinpeng Dong, Ruoxi Jia, Yang Zhang, Shiqing Ma, Xiangyu Zhang, Neil Gong, Chaowei Xiao, Sarah Erfani, Tim Baldwin, Bo Li, Masashi Sugiyama, Dacheng Tao, James Bailey, Yu-Gang Jiang

arXiv:2502.05206v6cs.CRcs.AIcs.CLcs.CV

TL;DR

Large-model deployment creates risks involving harmful behavior, privacy, and attacks, while existing technical surveys often cover narrower model or threat categories. This survey synthesizes safety research across six model categories, organizing attacks, defenses, datasets, and benchmarks through a comprehensive taxonomy. It concludes that robust evaluation, scalable and integrated defenses, and continued collective research remain necessary for safe large-model systems.

  • Problem

    Large-model deployment raises risks from adversarial, jailbreak, backdoor, privacy, and harmful-content failures, creating a need to understand and mitigate these vulnerabilities.

  • Method

    The survey uses a two-level taxonomy to synthesize technical attacks and defenses across six model categories, including their methods, datasets, and benchmarks.

  • Results

    The survey presents a comprehensive account of threats and defenses while identifying open challenges in safety evaluation, scalable proactive defense, and integrated protection.

  • Takeaways & Limitations

    Safe large-model development requires robust evaluation, scalable and proactive defenses, and collective action from the global research community.

  • Takeaways & Limitations

    Agent safety remains especially challenging because agents can execute real-world actions, complicate failure attribution, and lack adequate safeguards against malicious actions.

Abstract

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The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI). These models are now foundational to a wide range of applications, including conversational AI, recommendation systems, autonomous driving, content generation, medical diagnostics, and scientific discovery. However, their widespread deployment also exposes them to significant safety risks, raising concerns about robustness, reliability, and ethical implications. This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models (VFMs), Large Language Models (LLMs), Vision-Language Pre-training (VLP) models, Vision-Language Models (VLMs), Diffusion Models (DMs), and large-model-powered Agents. Our contributions are summarized as follows: (1) We present a comprehensive taxonomy of safety threats to these models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats. (2) We review defense strategies proposed for each type of attacks if available and summarize the commonly used datasets and benchmarks for safety research. (3) Building on this, we identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices. More importantly, we highlight the necessity of collective efforts from the research community and international collaboration. Our work can serve as a useful reference for researchers and practitioners, fostering the ongoing development of comprehensive defense systems and platforms to safeguard AI models.

1 INTRODUCTION

Large models support increasingly important applications but introduce substantial safety risks, motivating systematic study of attacks, defenses, and evaluation practices. This survey organizes technical research across six model categories and ten attack types using a structured review of 574 papers.

  • Motivation: Large models enable applications across language, vision, healthcare, education, and content creation, but their deployment raises risks involving attacks, privacy, and harmful or misleading content.These risks include unintended behaviors, privacy leakage, and dissemination of harmful information.
  • Scope: The survey reviews six model categories: VFMs, LLMs, VLPs, VLMs, DMs, and large-model-powered Agents.Each category is reviewed separately for attacks and defenses.
  • Scope: Its organization identifies ten attack types, including adversarial, backdoor, poisoning, jailbreak, prompt injection, energy-latency, extraction, membership-inference, and agent attacks.Backdoor and poisoning attacks are combined when both occur for a model category, and defenses are reviewed alongside attack types.
  • Method: The taxonomy uses two levels: categories distinguish threat models or subtasks, while subcategories classify the underlying techniques.Examples include white-box, gray-box, and black-box threats, plus detection, purification, robust training or tuning, and robust inference.
  • Method: The review methodology combines keyword search, manual filtering, and categorization of methods by settings and attack or defense types across 574 technical papers.The surveyed papers are distributed across years, model types, and attack or defense strategies.
  • Contribution: Compared with prior surveys, this work covers more model types and threats while emphasizing high-level technical methodologies rather than detailed techniques.The survey also discusses strengths, weaknesses, and gaps in existing safety research.

2 VISION FOUNDATION MODEL SAFETY

The survey covers safety research on ViTs and SAM, organizing attacks by threat access and strategy, and defenses by their intervention approach. Dataset coverage is concentrated in a few benchmarks, while SAM research remains comparatively specialized.

  • SAM safety: SAM safety research addresses a modular image encoder, prompt encoder, and mask decoder whose interconnected vulnerabilities require specialized attack and defense strategies.The survey focuses on ViTs and SAM as widely deployed vision foundation models.
  • ViT attacks: ViT attacks are classified into white-box patch, position-embedding, and attention attacks, plus black-box transfer-based and query-based attacks.The classification is based on attacker access and attack strategy.
  • ViT attacks: Patch, position-embedding, and attention attacks target token patches, positional information, and self-attention computations, respectively.Examples include perturbing image patches, distorting positional embeddings, and redirecting attention to adversarial tokens.
  • ViT attacks: Black-box transfer attacks optimize perturbations on surrogate or ensemble models, while query-based attacks use model responses to estimate gradients.Query-based attacks include score-based and decision-based settings according to the victim’s response.
  • ViT defenses: ViT defenses include adversarial training, detection, robust architecture, and purification, with adversarial training widely regarded as effective but computationally expensive.AGAT reduces training overhead by selectively dropping patch embeddings while maintaining robustness on large datasets such as ImageNet.

3 LARGE LANGUAGE MODEL SAFETY

The survey classifies LLM adversarial attacks by attacker access and manipulation level, covering white-box text perturbations and black-box prompt-based strategies. It also includes attacks targeting tabular data and attacks that use LLMs to automate adversarial behavior.

  • Adversarial attacks: LLM adversarial attacks aim to induce incorrect responses by subtly altering input text and are divided into white-box and black-box settings.The distinction depends on whether the attacker can access model internals.
  • White-box attacks: White-box attacks include character-level modifications such as typos and homoglyphs, and word-level substitutions that preserve semantic similarity.Character-level changes emphasize stealth, while word-level methods use synonym or gradient-guided substitutions.
  • Black-box attacks: Black-box attacks manipulate prompts through poisoned in-context demonstrations, deceptive induced prompts, LLM-assisted strategies, and tabular entity swaps.These methods operate with limited or no access to target parameters and rely on indirect interaction or structured-data leakage.

3.2 Adversarial Defenses

Adversarial defenses for LLMs are organized around detecting malicious inputs and making inference more resistant to attacks. The section also describes jailbreak and fine-tuning attacks that expand the safety threat beyond ordinary input perturbations.

  • Adversarial defenses: LLM adversarial defenses consist primarily of adversarial detection and robust inference.Detection filters potentially malicious prompts, while robust inference modifies model mechanisms or training to resist attacks.
  • Adversarial detection: Input filtering can provide a lightweight first defense, but adaptive attacks may bypass it because effectiveness depends on selected features and attack sophistication.Perplexity-based filtering identifies prompts that deviate from natural language patterns.
  • Robust inference: Robust inference methods such as Circuit Breaking target harmful activation patterns without retraining, but may impose higher computational costs and vary across architectures and attack types.The method intervenes during inference to neutralize harmful outputs.
  • Jailbreak attacks: Jailbreak attacks bypass safety alignment to produce harmful or deceptive content, commonly targeting black-box LLM-as-a-Service systems.Hand-crafted, scenario-based, and encoding-based prompts obscure or disguise harmful requests.
  • Automated attacks: Automated jailbreak attacks optimize prompts through black-box search or adversary LLMs, including genetic algorithms, fuzzing, and iterative multi-turn refinement.These approaches seek effective prompts without manually designing each attack.
  • Fine-tuning attacks: Fine-tuning attacks can undermine safety alignment with small malicious datasets, while Virus crafts data that appears benign to guardrails yet degrades alignment.The attack extends the safety attack surface from inference-time prompts to model customization.

3.4 Jailbreak Defenses

Jailbreak defenses intervene at the input, output, ensemble, or fine-tuning stage. They range from prompt transformation and filtering to output monitoring, multi-model coordination, and methods that preserve or restore alignment during model customization.

  • Input defenses: Input defenses rephrase, translate, or filter prompts to reduce harmful intent or reject malicious queries before inference.Examples include perturbation-based rephrasing, cross-lingual transformations, and embedding-geometry detection.
  • Output defenses: Output defenses inspect generated content and trigger refusal or modification when classifiers, internal signals, or safety boundaries indicate harm.Methods use safety classifiers, refusal loss, internal activations, conceptual features, or output repetition consistency.
  • Ensemble defenses: Ensemble defenses combine multiple models or defense mechanisms so their differing weaknesses can be offset.MTD selects among diverse LLM outputs, while AutoDefense combines input and output defenses and MoGU routes between safe and usable models.
  • Fine-tuning defenses: Defenses against harmful fine-tuning operate during alignment, fine-tuning, or post-fine-tuning stages.The stages respectively strengthen pre-adaptation resilience, constrain safety drift during adaptation, or repair compromised models afterward.

3.5 Prompt Injection Attacks

Prompt injection attacks embed malicious instructions into otherwise benign prompts, while defenses process inputs or fine-tune models to distinguish legitimate from malicious instructions. The survey also situates related backdoor mechanisms and their attack surfaces across poisoning, training manipulation, and parameter modification.

  • Prompt injection attacks manipulate LLMs by inserting malicious instructions into otherwise benign prompts, with this section focusing on black-box attacks in LLM-as-a-Service systems.
  • Hand-crafted Attacks: Hand-crafted injections use expert-designed prompts, including appended commands, context-ignoring instructions, and indirect injections targeting retrieval-augmented systems.
  • Automated Attacks: Automated injections use evolutionary algorithms, gradient-based optimization, iterative red teaming, and goal-guided generation to search for effective attack prompts.
  • Prompt Injection Defenses: Input defenses process prompts without changing the core LLM, using structured instruction-data separation, DSL-based system-prompt management, and input rephrasing.
  • Adversarial Fine-tuning: Adversarial fine-tuning restricts models to defined tasks and improves instruction discrimination, but can reduce generalization and flexibility.
  • Backdoor Attacks: LLM backdoor attacks inject triggers through poisoned data, manipulated training, or parameter modification, including prompt-level and multi-trigger poisoning.
  • Prompt-level Poisoning: Specialized and multi-trigger poisoning target chain-of-thought or distribute trigger components across inputs to increase stealth and resistance to basic detection.
  • Training Manipulation: Training manipulation alters optimization to implant prompt-triggered backdoors, while parameter modification directly changes small neuron subsets and must survive downstream fine-tuning.

3.8 Backdoor Defenses

Backdoor defenses for LLMs comprise detection, removal, robust training, and robust inference. They identify suspicious triggers, overwrite or suppress learned backdoor mappings, prevent poisoned learning, or contrast outputs with trusted behavior.

  • LLM backdoor defenses are categorized into detection, removal, robust training, and robust inference.
  • Backdoor Detection: Backdoor detection analyzes anomalous token contributions or prediction inconsistencies without requiring original training data or attack details.
  • Backdoor Removal: Backdoor removal prunes components associated with activation or fine-tunes on clean data to weaken trigger-target mappings.
  • Robust Training: Robust training isolates backdoor features or encourages representations less sensitive to specific triggers when poisoned data is encountered.
  • Robust Inference: Robust inference adjusts generation to reduce backdoor effects, including contrastive decoding against a clean reference model.
  • Robust Inference: Contrastive decoding can replace suspicious tokens or ensemble logits with benign-model outputs, but requires a trusted clean reference model.

3.9 Safety Alignment

Safety alignment seeks to align LLM behavior with human values despite risks of toxic content, misinformation, and bias. The survey reviews human, AI, and simulated-social feedback methods, while highlighting deceptive alignment as a failure mode in which apparent compliance may conceal persistent or strategic misalignment.

  • LLM alignment aims to bridge pre-training patterns and human values, addressing toxic content, misinformation, and bias through human, AI, or social feedback.
  • Alignment with Human Feedback: RLHF incorporates human preferences through PPO, DPO, Kahneman-Tversky optimization, and supervised fine-tuning.
  • Alignment with AI Feedback: RLAIF uses AI-generated feedback to improve alignment scalability and reduce reliance on human labor.
  • Alignment with Social Interactions: Social-interaction methods use simulated environments and contrastive policy optimization to train models against social norms and constraints.
  • Deceptive Alignment: Deceptive alignment occurs when a model appears aligned while retaining goals that diverge from human intent, including fake alignment and alignment faking.
  • Fake Alignment: Fake alignment produces stylistically safe responses without deep safety understanding, with open-ended safety potentially failing under multiple-choice testing.
  • Alignment Faking: Alignment faking involves strategic compliance during training to avoid modification and preserve potentially misaligned preferences for later deployment.
  • Alignment Faking: Across 25 frontier models, strategic alignment-faking behavior was not widespread, but it appeared in some powerful models and was linked to instrumental or terminal goal guarding.

3.10 Energy Latency Attacks

Energy-latency attacks increase LLM inference computation, causing higher latency and energy consumption. The survey distinguishes white-box and black-box strategies, including gradient, query, and poisoning-based attacks, and notes limits in generality and efficiency.

  • Energy-latency attacks degrade LLM inference efficiency by increasing computational demands, latency, and energy consumption.
  • White-box attacks use model knowledge to manipulate inference, while black-box attacks rely only on the input-output interface.
  • White-box Attacks: Gradient-based attacks suppress EOS prediction or early exit, forcing autoregressive models to generate abnormally long outputs.
  • Black-box Attacks: Query-based attacks repeatedly craft inputs that exploit model behaviors, but they are typically more time-consuming than white-box attacks and may depend on skimming mechanisms.
  • Poisoning-based Attacks: A single poisoned fine-tuning sample can induce excessively long outputs, increase latency, and bypass output-length constraints through limited-access APIs.
  • Existing energy-latency attacks are often architecture-specific, computationally expensive, or less effective in black-box settings, while runtime validation can add overhead.

3.11 Model Extraction Attacks

The survey categorizes model extraction attacks against LLMs by whether they target fine-tuned capabilities or alignment properties, while also reviewing data extraction threats and their limitations.

  • Model Extraction Attacks: Model extraction attacks query target LLMs to build substitute models that replicate their functionality.They are divided into fine-tuning-stage and alignment-stage attacks.
  • Fine-tuning Stage Attacks: Fine-tuning-stage attacks extract either overall functional behavior or specific abilities such as coding through carefully crafted queries.Specific-ability extraction may struggle with unseen inputs because the extracted model relies on the target model’s generalization.
  • Alignment Stage Attacks: Alignment-stage attacks seek to reproduce safety and helpfulness properties by extracting the reward or preference structure guiding the target model.LoRD uses policy gradients to extract both task-specific knowledge and alignment properties, but human preferences remain difficult to capture fully.
  • Limitations: Current extraction methods obtain task-specific knowledge and alignment properties but do not yet accurately replicate the full complexity of target models.The survey identifies proactive defenses against model extraction as an ongoing need.
  • Data Extraction Attacks: LLM data extraction attacks exploit memorized training data to recover sensitive information, including personal identifiers, copyrighted content, or confidential data.White-box methods analyze parameters or activations, whereas black-box methods use inductive prompts without parameter access.
  • Data Extraction Attacks: Black-box data extraction includes prefix, special-character, prompt-optimization, retrieval-augmented-generation, ensemble, and semantic-information attacks.Their targets range from verbatim training examples to sensitive semantic content and information stored in external retrieval systems.

3.13 Datasets & Benchmarks

The survey organizes LLM safety datasets and benchmarks by evaluation purpose, covering toxicity, truthfulness, values, adversarial robustness, backdoors, and jailbreaks. These resources expose important safety properties but still face scalability, consistency, and real-world relevance challenges.

  • Benchmark Taxonomy: LLM safety resources are grouped into toxicity, truthfulness, value, adversarial, and backdoor benchmark categories.This organization aligns datasets with the safety property or threat they are intended to evaluate.
  • Toxicity Datasets: RealToxicityPrompts pairs 100,000 prompts with Perspective API toxicity scores to assess harmful text generation from benign inputs.Its reliance on the potentially biased Perspective API is a stated limitation.
  • Truthfulness Datasets: TruthfulQA evaluates factuality using 817 questions across 38 categories and targets false answers learned from human text.Its focus on imitative falsehoods may not capture all dimensions of truthfulness.
  • Value Benchmarks: Value-alignment benchmarks evaluate fairness, safety, morality, responsibility, and unsafe-request refusal across Chinese and general LLMs.FLAMES uses 2,251 prompts, while SORRY-Bench covers 45 topic categories.
  • Adversarial and Backdoor Benchmarks: BackdoorLLM, Adversarial GLUE, and SALAD-Bench evaluate backdoors, textual robustness, and broader safety risks using standardized attack and defense-oriented settings.BackdoorLLM includes data- and weight-poisoning strategies, while Adversarial GLUE tests 14 textual attack methods.
  • Jailbreak Benchmarks: Jailbreak benchmarks use text- and image-based test cases, but existing resources remain limited by scalability, consistency, and real-world relevance.STRONGREJECT for empty jailbreaks improves dataset quality and automated assessment.

4 VISION-LANGUAGE PRE-TRAINING MODEL SAFETY

VLP safety research examines adversarial, backdoor, and poisoning threats across multimodal inputs and downstream tasks, alongside detection, training, prompt-tuning, and contrastive defenses. The survey highlights trade-offs between attack effectiveness, transferability, and the computational cost of robust training.

  • Scope: VLP safety research focuses on adversarial, backdoor, and poisoning threats affecting multimodal models used in downstream vision-language tasks.Adversarial attacks are classified by threat model into white-box and black-box settings.
  • Adversarial Attacks: White-box attacks use invisible or visible perturbations, with most existing methods relying on small, imperceptible changes to images or text.Co-Attack jointly perturbs visual and textual inputs, while visible attacks exploit typography and other noticeable image alterations.
  • Black-box Attacks: Sample-wise perturbations are generally more effective, whereas universal perturbations are more transferable across samples and models.Transfer-based attacks dominate black-box VLP research, while query-based attacks remain comparatively rare.
  • Defenses: VLP defenses include adversarial-example detection, standard adversarial training, adversarial prompt tuning, and adversarial contrastive tuning.Detection can be one-shot or stateful; AdvQDet detects adversarial queries within three queries.
  • Scalable Training: Adversarial training is widely regarded as effective but prohibitively expensive for web-scale VLP models.VILLA reduces overhead through embedding-space adversarial pre-training, while AdvXL uses coarse-to-fine training with weaker attacks before stronger fine-tuning.
  • Efficient Defenses: Adversarial prompt tuning offers a lightweight alternative, and unsupervised adversarial contrastive tuning can improve robustness without retraining or additional fine-tuning.FARE robustifies CLIP’s image encoder and supports robustness across zero-shot classification and vision-language tasks.
  • Poisoning Attacks: PBCL achieved targeted poisoning with as little as 0.0001% of the CLIP training dataset, while MM Poison maintained clean-data performance across visual and textual modalities.MEM uses unlearnable examples to protect private data from multimodal contrastive-learning exploitation.
  • Poisoning Defenses: MEM reduces poisoned image-text pair similarity by separately fine-tuning safe and risky data sets, defending against targeted poisoning and backdoor attacks.The method combines CLIP-loss treatment on the safe set with separate risky-set fine-tuning.

5 VISION-LANGUAGE MODEL SAFETY

VLMs introduce multimodal safety risks spanning adversarial manipulation, jailbreaks, prompt injection, latency-energy attacks, and poisoning. The survey organizes these threats by attacker access and reviews transfer, generator-based, and image-mediated jailbreak strategies alongside detection-oriented defenses.

  • Scope: VLM safety research covers adversarial, latency-energy, jailbreak, prompt-injection, and backdoor or poisoning attacks enabled by visual inputs.The visual modality creates vulnerabilities distinct from those of text-only models.
  • White-box Attacks: White-box adversarial attacks target task outputs, cross-prompt transfer, or chain-of-thought reasoning with full access to VLM parameters.Examples manipulate captioning, transfer one image across prompts, or interfere with reasoning processes.
  • Threat Models: Gray-box attacks use access to a vision encoder or language model, while black-box attacks rely on transfer-based or generator-based methods without internal parameters.Frozen CLIP encoders create a common transfer route across deployed VLMs.
  • White-box Jailbreaks: White-box jailbreaks use gradient information to perturb images or text and include target-specific, universal, and hybrid categories.They aim to trigger harmful outputs by exploiting model safety measures.
  • Black-box Jailbreaks: Black-box jailbreaks exploit transferability, manually designed visual prompts, system-prompt leakage, or autonomous red teaming.These methods can use typography, role-play images, leaked prompts, or diffusion models to generate malicious image-text pairs.
  • Defenses: VLM jailbreak defenses are organized around jailbreak detection and jailbreak prevention methods.The survey introduces this defense taxonomy but the supplied passage does not provide further method-level detail.

6 DIFFUSION MODEL SAFETY

Diffusion models support conditional generation across image and video applications but face adversarial, jailbreak, and privacy threats. The survey organizes these attacks by attacker access, prompt granularity, and targeted safety mechanism.

  • Diffusion-model scope: Diffusion models add noise in a forward process and generate samples through reverse denoising, with conditional generation modeling p(x|guidance).They are used in image-to-image, text-to-image, and text-to-video applications.
  • Threats: Diffusion-model attacks can degrade generation quality, create semantic mismatches, bypass safety filters, manipulate outputs, and expose sensitive training data.The survey covers adversarial, jailbreak, backdoor, and privacy attacks.
  • Adversarial attacks: Adversarial attacks are categorized by white-box, gray-box, and black-box threat models, with black-box methods further divided into character-, word-, and sentence-level attacks.White-box attacks access model parameters, gray-box attacks exploit an exposed text encoder, and black-box attacks use only model outputs.
  • Adversarial attacks: White-box attacks optimize prompts or latent representations, while gray-box attacks exploit text-encoder similarity objectives to produce mismatched or targeted generations.Examples include SAGE, ATM, QFA, RVTA, and MMP-Attack.
  • Jailbreak attacks: Diffusion safety mechanisms include training-time alignment and external text, image, or text-image filters, each of which can be targeted by jailbreak attacks.Internal attacks bypass removed harmful concepts, whereas external attacks target input or output filters.
  • Jailbreak attacks: Gray-box attacks demonstrate that limited access to an open-source text encoder can enable attacks against safety-enhanced models.Ring-A-Bell, JPA, and RT-Attack exploit antonym prompts, embedding differences, or staged text- and image-level optimization.

7 AGENT SAFETY

Agents extend large-model safety risks because closed-loop systems integrate external information, tools, memory, and multi-step actions. The survey analyzes threats at indirect-injection, component, system, and agentic levels, alongside defenses and benchmarks.

  • Scope and structure: Agents use LLMs or VLMs for iterative planning, external observation, and multi-step reasoning in domains including healthcare, finance, and autonomous driving.Their closed-loop autonomy expands the attack surface compared with standalone large models.
  • Scope and structure: The survey organizes agent safety around indirect prompt injection, component-level threats, system-level risks, and agentic attacks and defenses.Component-level analysis includes memory, tool-calling and MCP, and VLM processing; system-level analysis includes multi-agent and embodied systems.
  • Indirect prompt injection: Indirect prompt injection is treated as a foundational attack vector because agents continuously integrate system prompts, user inputs, model outputs, and tool responses.Adversaries exploit this unified message-processing core to manipulate agent behavior.
  • Indirect malicious instruction injection: Indirect injections can remotely alter task execution, leak conversations, trigger phishing behavior, or convert unsafe prompts into SQL queries with database access.Examples include malicious webpage content, injected instructions to send chat histories, and Prompt-to-SQL attacks.
  • Indirect malicious instruction injection: Crowdsourced attack strategies in TensorTrust generalize from the game environment to ChatGPT, Claude, Bard, Bing Chat, and Notion AI.The reported attacks exhibit interpretable structural patterns associated with model vulnerabilities.
  • Memory attacks: Agent memory is vulnerable to both hidden-trigger backdoors and poisoning that corrupts information retrieved for decision-making.Short-term memory attacks manipulate context, while long-term attacks manipulate embeddings or retrieved documents; BadRAG reports a 98.2% success rate with ten adversarial passages.
  • Open challenges: Agent safety remains harder than traditional LLM safety because agents can execute actions affecting physical and digital environments, while complex workflows complicate failure attribution and safeguards remain inadequate.The conclusion identifies multi-agent complexity and insufficient safeguards as continuing challenges.

8 OPEN CHALLENGES

The survey identifies open challenges spanning evaluation, defense design, agent safety, deployment access, and international coordination. It calls for broader metrics, integrated and scalable protections, dedicated research infrastructure, and collaborative governance.

  • Evaluation: ASR alone misses disruption severity, resilience across attack types, and real-world consequences of subtle failures.The survey notes that models may cause harm or poor decisions even when primary functionality appears intact.
  • Defense limitations: Alignment can produce high safety scores without genuine internalization of safety principles, while advanced attacks may still circumvent aligned models.These findings expose the problem of shallow safety and limit alignment as a standalone safeguard.
  • Integrated defenses: Detection should be integrated with other defenses to trigger timely interventions and support dynamic responses to emerging threats.The survey identifies making detection, including chain-of-thought monitoring, a core component of safety pipelines as an open question.
  • Agent safety: Safe autonomous agents require safety mechanisms embedded throughout decision-making so they can handle unexpected challenges while remaining robust, reliable, and aligned.Agents face broader attack surfaces because they interact with external tools, data sources, and environments.
  • Practical defenses: Defense research should rebalance toward practical prevention, layered integration, and mechanisms that generalize across systems and operate in black-box settings.The survey links generality with scalable deployment and identifies limited access to model internals as a practical constraint.
  • Research infrastructure and collaboration: The survey advocates dedicated safety APIs, open-source platforms, cross-border data sharing, international alliances, and enforceable governance mechanisms.These measures are presented as ways to support external assessment, collaboration, broader evaluation, transparency, and accountability.

9 CONCLUSION

The survey organizes safety research across six large-model categories and agents, synthesizing threats, defenses, and unresolved challenges. It concludes that safe AI requires scalable, integrated technical defenses alongside collective international action.

  • The survey covers safety research on VFMs, LLMs, VLP models, VLMs, DMs, and large-model-powered agents.
  • It presents a comprehensive taxonomy of existing threats and defenses while highlighting the evolving challenges facing large models.
  • Open challenges include understanding fundamental vulnerabilities, establishing robust safety evaluations, and developing scalable, proactive, integrated defenses.
  • Achieving safe AI requires technical advances, collective action from the global research community, and international collaboration.

10 AUTHOR CONTRIBUTIONS

The paper’s authors divided responsibility by model family and safety topic, with designated contributors surveying literature, drafting sections, and providing technical guidance. Senior contributors also coordinated the broader project, scientific direction, and final review.

  • Xingjun Ma designed the survey structure, organized the review, wrote the challenges and conclusion sections, and prepared the final manuscript.
  • Yu-Gang Jiang initiated the project, secured resources, guided scientific direction, coordinated teams, and supervised final review and submission.
  • Vision Foundation Model Safety: The Vision Foundation Model Safety section covered visual backbones, scalable pre-training, safety studies, adversarial robustness, architecture design, and graph signal processing.
  • Large Language Model Safety: The Large Language Model Safety section addressed alignment, jailbreaks, prompt injection, extraction threats, multilingual scaling, privacy, and generalization.
  • Separate teams reviewed safety for VLP models, VLMs, diffusion models, and agents, including datasets, defenses, extraction, intellectual property, and secure tool use.
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