SUMMARY.md

Table of contents

• 前言
• MM-LLM
  • MM-LLMs: Recent Advances in MultiModal Large Language Models
  • Multimodal datasets: misogyny, pornography, and malignant stereotypes
  • Sight Beyond Text: Multi-Modal Training Enhances LLMsinTruthfulness and Ethics
  • FOUNDATION MODELS AND FAIR USE
• VLM-Defense
  • Removing NSFW Concepts from Vision-and-Language Models for Text-to-Image Retrieval and Generation
  • Safety Alignment for Vision Language Models
  • AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Sh
  • Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
  • MLLM-Protector: Ensuring MLLM’s Safety without Hurting Performance
  • Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
  • SAFEGEN: Mitigating Unsafe Content Generation in Text-to-Image Models
  • Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision
  • Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
  • Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
  • UNDERSTANDING ZERO-SHOT ADVERSARIAL ROBUSTNESS FOR LARGE-SCALE MODELS
  • A Mutation-Based Method for Multi-Modal Jailbreaking Attack Detection
  • UNDERSTANDING ZERO-SHOT ADVERSARIAL ROBUSTNESS FOR LARGE-SCALE MODELS
  • AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Sh
  • CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
  • Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content
  • Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models
  • Typographic Attacks in Large Multimodal Models Can be Alleviated by More Informative Prompts
  • Onthe Robustness of Large Multimodal Models Against Image Adversarial Attacks
  • Removing NSFW Concepts from Vision-and-Language Models for Text-to-Image Retrieval and Generation
  • Safety Fine-Tuning at (Almost) No Cost: ABaseline for Vision Large Language Models
  • Partially Recentralization Softmax Loss for Vision-Language Models Robustness
  • Adversarial Prompt Tuning for Vision-Language Models
  • Defense-Prefix for Preventing Typographic Attacks on CLIP
  • RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedb
  • AMutation-Based Method for Multi-Modal Jailbreaking Attack
  • HowEasy is It to Fool Your Multimodal LLMs? AnEmpirical Analysis on Deceptive Prompts
  • MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
  • EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large
  • Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
  • Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Lang
  • Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoisi
  • Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor Attacks
  • HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
• VLM
  • Scalable Performance Analysis for Vision-Language Models
• VLM-Attack
  • Circumventing Concept Erasure Methods For Text-to-Image Generative Models
  • Efficient LLM-Jailbreaking by Introducing Visual Modality
  • From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
  • Adversarial Attacks on Multimodal Agents
  • Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Ima
  • Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models
  • Typography Leads Semantic Diversifying: Amplifying Adversarial Transferability across Multimodal Lar
  • White-box Multimodal Jailbreaks Against Large Vision-Language Models
  • Red Teaming Visual Language Models
  • Private Attribute Inference from Images with Vision-Language Models
  • Assessment of Multimodal Large Language Models in Alignment with Human Values
  • Privacy-Aware Visual Language Models
  • Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbre
  • Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
  • Red Teaming Visual Language Models
  • Adversarial Illusions in Multi-Modal Embeddings
  • Universal Prompt Optimizer for Safe Text-to-Image Generation
  • On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
  • Adversarial Illusions in Multi-Modal Embeddings
  • Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images
  • INSTRUCTTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
  • On the Robustness of Large Multimodal Models Against Image Adversarial Attacks
  • Hijacking Context in Large Multi-modal Models
  • Transferable Multimodal Attack on Vision-Language Pre-training Models
  • Images are Achilles’ Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimoda
  • AN IMAGE IS WORTH 1000 LIES: ADVERSARIAL TRANSFERABILITY ACROSS PROMPTS ON VISIONLANGUAGE MODELS
  • Test-Time Backdoor Attacks on Multimodal Large Language Models
  • JAILBREAK IN PIECES: COMPOSITIONAL ADVERSARIAL ATTACKS ON MULTI-MODAL LANGUAGE MODELS
  • Jailbreaking Attack against Multimodal Large Language Model
  • Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts
  • IMAGE HIJACKS: ADVERSARIAL IMAGES CAN CONTROL GENERATIVE MODELS AT RUNTIME
  • VISUAL ADVERSARIAL EXAMPLES JAILBREAK ALIGNED LARGE LANGUAGE MODELS
  • Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
  • Query-Relevant Images Jailbreak Large Multi-Modal Models
  • Towards Adversarial Attack on Vision-Language Pre-training Models
  • HowMany Are Unicorns in This Image? ASafety Evaluation Benchmark for Vision LLMs
  • SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Au
  • MISUSING TOOLS IN LARGE LANGUAGE MODELS WITH VISUAL ADVERSARIAL EXAMPLES
  • VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models
  • INSTRUCTTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
  • Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Mod
  • Shadowcast: STEALTHY DATA POISONING ATTACKS AGAINST VISION-LANGUAGE MODELS
  • FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts
  • THE WOLF WITHIN: COVERT INJECTION OF MALICE INTO MLLM SOCIETIES VIA AN MLLM OPERATIVE
  • Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images
  • Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
  • How Robust is Google’s Bard to Adversarial Image Attacks?
  • OnEvaluating Adversarial Robustness of Large Vision-Language Models
  • Onthe Adversarial Robustness of Multi-Modal Foundation Models
  • Are aligned neural networks adversarially aligned?
  • READING ISN’T BELIEVING: ADVERSARIAL ATTACKS ON MULTI-MODAL NEURONS
  • Black Box Adversarial Prompting for Foundation Models
  • Evaluation and Analysis of Hallucination in Large Vision-Language Models
  • FOOL YOUR (VISION AND) LANGUAGE MODEL WITH EMBARRASSINGLY SIMPLE PERMUTATIONS
  • VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models
  • Transferable Multimodal Attack on Vision-Language Pre-training Models
  • BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning
  • AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
• T2I-Attack
  • On Copyright Risks of Text-to-Image Diffusion Models
  • ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users
  • On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
  • Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
  • SneakyPrompt: Jailbreaking Text-to-image Generative Models
  • The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breach
  • Discovering Universal Semantic Triggers for Text-to-Image Synthesis
  • Automatic Jailbreaking of the Text-to-Image Generative AI Systems
• Survey
  • Generative AI Security: Challenges and Countermeasures
  • Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
  • Current state of LLM Risks and AI Guardrails
  • Security of AI Agents
  • Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
  • Exploring Vulnerabilities and Protections in Large Language Models: A Survey
  • Unveiling Hallucination in Text, Image, Video, and Audio Foundation Models: A Comprehensive Survey
  • Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Mode
  • SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Mode
  • Safety of Multimodal Large Language Models on Images and Text
  • LLM Jailbreak Attack versus Defense Techniques - A Comprehensive Study
  • Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks
  • ASurvey on Safe Multi-Modal Learning System
  • TRUSTWORTHY LARGE MODELS IN VISION: A SURVEY
  • A Pathway Towards Responsible AI Generated Content
  • A Survey of Hallucination in “Large” Foundation Models
  • An Early Categorization of Prompt Injection Attacks on Large Language Models
  • Comprehensive Assessment of Jailbreak Attacks Against LLMs
  • A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks
  • Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks
  • Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
  • Red-Teaming for Generative AI: Silver Bullet or Security Theater?
  • A STRONGREJECT for Empty Jailbreaks
• LVM-Attack
  • Adversarial Attacks on Foundational Vision Models
• For Good
  • Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content
• Benchmark
  • HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusi
  • OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
  • ToViLaG: Your Visual-Language Generative Model is Also An Evildoer
  • HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
  • S-Eval: Automatic and Adaptive Test Generation for Benchmarking Safety Evaluation of Large Language
  • UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images
  • JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against
  • JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
  • Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs
  • ALERT: A Comprehensive Benchmark for Assessing Large Language Models’ Safety through Red Teaming
  • Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Halluc
  • INJECAGENT: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
  • AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Adversarial Visual-Ins
  • HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusio
  • ALL LANGUAGES MATTER: ON THE MULTILINGUAL SAFETY OF LARGE LANGUAGE MODELS
  • Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial
  • Red Teaming Visual Language Models
  • Unified Hallucination Detection for Multimodal Large Language Models
  • MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
  • Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
  • CAN LANGUAGE MODELS BE INSTRUCTED TO PROTECT PERSONAL INFORMATION?
  • Detecting and Preventing Hallucinations in Large Vision Language Models
  • DRESS : Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Lang
  • ToViLaG: Your Visual-Language Generative Model is Also An Evildoer
  • SC-Safety: A Multi-round Open-ended Question Adversarial Safety Benchmark for Large Language Models
  • PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
  • Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
• Explainality
  • Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attributio
• Privacy-Defense
  • Defending Our Privacy With Backdoors
  • PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification
• Privacy-Attack
  • PANDORA’S WHITE-BOX: INCREASED TRAINING DATA LEAKAGE IN OPEN LLMS
  • Untitled
  • Membership Inference Attacks against Large Language Models via Self-prompt Calibration
  • LANGUAGE MODEL INVERSION
  • Effective Prompt Extraction from Language Models
  • Prompt Stealing Attacks Against Large Language Models
  • Stealing Part of a Production Language Model
  • Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Cali
  • Prompt Stealing Attacks Against Large Language Models
  • PRSA: Prompt Reverse Stealing Attacks against Large Language Models
  • Low-Resource Languages Jailbreak GPT-4
  • Scalable Extraction of Training Data from (Production) Language Models
• Others
  • INFERRING OFFENSIVENESS IN IMAGES FROM NATURAL LANGUAGE SUPERVISION
  • An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulne
  • More RLHF, More Trust? On The Impact of Human Preference Alignment On Language Model Trustworthiness
  • AI SAFETY: A CLIMB TO ARMAGEDDON?
  • AI RISK MANAGEMENT SHOULD INCORPORATE BOTH SAFETY AND SECURITY
  • Defending Against Social Engineering Attacks in the Age of LLMs
  • Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI
  • Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
  • Deduplicating Training Data Makes Language Models Better
  • MITIGATING TEXT TOXICITY WITH COUNTERFACTUAL GENERATION
  • The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
  • Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
  • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
  • Mitigating LLM Hallucinations via Conformal Abstention
  • Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
  • Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
  • An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
  • Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
  • LARGE LANGUAGE MODELS AS AUTOMATED ALIGNERS FOR BENCHMARKING VISION-LANGUAGE MODELS
  • PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
  • Reducing hallucination in structured outputs via Retrieval-Augmented Generation
  • Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision
  • Attacking LLM Watermarks by Exploiting Their Strengths
  • The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Mod
  • TOFU: A Task of Fictitious Unlearning for LLMs
  • Learning and Forgetting Unsafe Examples in Large Language Models
  • Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Tra
  • TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
  • In Search of Truth: An Interrogation Approach to Hallucination Detection
  • Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
  • Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
  • Locating and Mitigating Gender Bias in Large Language Models
  • Learning to Edit: Aligning LLMs with Knowledge Editing
  • Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
  • Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Su
  • Does DETECTGPT Fully Utilize Perturbation? Bridge Selective Perturbation to Fine-tuned Contrastive L
  • TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
  • SPOTTING LLMS WITH BINOCULARS: ZERO-SHOT DETECTION OF MACHINE-GENERATED TEXT
  • LLM-as-a-Coauthor: The Challenges of Detecting LLM-Human Mixcase
  • WHAT’S IN MY BIG DATA?
  • UNDERSTANDING CATASTROPHIC FORGETTING IN LANGUAGE MODELS VIA IMPLICIT INFERENCE
  • Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models
  • Toxicity in CHATGPT: Analyzing Persona-assigned Language Models
  • MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation
  • Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision
  • Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models
  • Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers’ Coding Practices with Insecure Sug
  • Zero shot VLMs for hate meme detection: Are we there yet?
  • ANALYZING AND MITIGATING OBJECT HALLUCINATION IN LARGE VISION-LANGUAGE MODELS
  • MITIGATING HALLUCINATION IN LARGE MULTIMODAL MODELS VIA ROBUST INSTRUCTION TUNING
  • DENEVIL: TOWARDS DECIPHERING AND NAVIGATING THE ETHICAL VALUES OF LARGE LANGUAGE MODELS VIA INSTRUCT
  • Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates
  • Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
  • LARGE LANGUAGE MODELS AS AUTOMATED ALIGNERS FOR BENCHMARKING VISION-LANGUAGE MODELS
  • Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Pro
  • InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
  • CAN LANGUAGE MODELS BE INSTRUCTED TO PROTECT PERSONAL INFORMATION?
  • AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications
  • Prompt Injection Attacks and Defenses in LLM-Integrated Applications
  • Removing RLHF Protections in GPT-4 via Fine-Tuning
  • SPML: A DSL for Defending Language Models Against Prompt Attacks
  • Stealthy Attack on Large Language Model based Recommendation
  • Large Language Models Sometimes Generate Purely Negatively-Reinforced Text
  • On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
  • Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring t
  • longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A system
  • A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
  • Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models
  • Discovering the Hidden Vocabulary of DALLE-2
  • Raising the Cost of Malicious AI-Powered Image Editing
  • Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimi
  • ALIGNERS: DECOUPLING LLMS AND ALIGNMENT
  • CAN LLM-GENERATED MISINFORMATION BE DETECTED?
  • On the Risk of Misinformation Pollution with Large Language Models
  • Evading Watermark based Detection of AI-Generated Content
  • Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World’s Uglin
  • Privacy-Preserving Instructions for Aligning Large Language Models
  • TOWARDS UNDERSTANDING THE INTERPLAY OF GENERATIVE ARTIFICIAL INTELLIGENCE AND THE INTERNET
  • Evaluating the Social Impact of Generative AI Systems in Systems and Society
  • Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
  • Attacking LLM Watermarks by Exploiting Their Strengths
  • TOWARDS RESPONSIBLE AI IN THE ERA OF GENERATIVE AI: A REFERENCE ARCHITECTURE FOR DESIGNING FOUNDATIO
  • RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
  • Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safet
  • Risk Assessment and Statistical Significance in the Age of Foundation Models
  • The Foundation Model Transparency Index
  • The Privacy Pillar - A Conceptual Framework for Foundation Model-based Systems
  • A Baseline Analysis of Reward Models’ Ability To Accurately Analyze Foundation Models Under Distribu
  • Foundational Moral Values for AI Alignment
  • Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
  • ON CATASTROPHIC INHERITANCE OF LARGE FOUNDATION MODELS
  • Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
  • Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustmen
  • Foundation Model Transparency Reports
  • SECURING RELIABILITY: A BRIEF OVERVIEW ON ENHANCING IN-CONTEXT LEARNING FOR FOUNDATION MODELS
  • EXPLORING THE ADVERSARIAL CAPABILITIES OF LARGE LANGUAGE MODELS
  • TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
  • LLM-Resistant Math Word Problem Generation via Adversarial Attacks
  • Efficient Black-Box Adversarial Attacks on Neural Text Detectors
  • Adversarial Preference Optimization
  • Combating Adversarial Attacks with Multi-Agent Debate
  • How the Advent of Ubiquitous Large Language Models both Stymie and Turbocharge Dynamic Adversarial Q
  • L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
  • Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
  • What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectio
  • Prompted Contextual Vectors for Spear-Phishing Detection
  • Token-Ensemble Text Generation: On Attacking the Automatic AI-Generated Text Detection
  • Recursive Chain-of-Feedback Prevents Performance Degradation from Redundant Prompting
  • Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
  • RADAR: Robust AI-Text Detection via Adversarial Learning
  • OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examp
  • Why do universal adversarial attacks work on large language models?: Geometry might be the answer
  • J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News
  • Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge
  • Detoxifying Large Language Models via Knowledge Editing
  • Healing Unsafe Dialogue Responses with Weak Supervision Signals
• LLM-Attack
  • Hacc-Man: An Arcade Game for Jailbreaking LLMs
  • Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
  • DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
  • Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak
  • Hijacking Large Language Models via Adversarial In-Context Learning
  • Make Them Spill the Beans! Coercive Knowledge Extraction from (Production) LLMs
  • DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models
  • FRONTIER LANGUAGE MODELS ARE NOT ROBUST TO ADVERSARIAL ARITHMETIC, OR “WHAT DO I NEED TO SAY SO YOU
  • Backdoor Activation Attack: Attack Large Language Models using Activation Steering for Safety-Alignm
  • Evil Geniuses: Delving into the Safety of LLM-based Agents
  • BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B
  • SHADOW ALIGNMENT: THE EASE OF SUBVERTING SAFELY-ALIGNED LANGUAGE MODELS
  • Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
  • ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger
  • Tastle: Distract Large Language Models for Automatic Jailbreak Attack
  • Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue
  • Learning to Poison Large Language Models During Instruction Tuning
  • TALK TOO MUCH: Poisoning Large Language Models under Token Limit
  • Don’t Say No: Jailbreaking LLM by Suppressing Refusal
  • Goal-guided Generative Prompt Injection Attack on Large Language Models
  • Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
  • BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents
  • AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens
  • QROA: A Black-Box Query-Response Optimization Attack on LLMs
  • BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models
  • Improved Generation of Adversarial Examples Against Safety-aligned LLMs
  • Exploring Backdoor Attacks against Large Language Model-based Decision Making
  • Jailbreak Paradox: The Achilles’ Heel of LLMs
  • Stealth edits for provably fixing or attacking large language models
  • Stealth edits for provably fixing or attacking large language models
  • IS POISONING A REAL THREAT TO LLM ALIGNMENT? MAYBE MORE SO THAN YOU THINK
  • IS POISONING A REAL THREAT TO LLM ALIGNMENT? MAYBE MORE SO THAN YOU THINK
  • Knowledge-to-Jailbreak: One Knowledge Point Worth One Attack
  • “Not Aligned” is Not “Malicious”: Being Careful about Hallucinations of Large Language Models’ Jailb
  • Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis
  • Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
  • Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs
  • StructuralSleight: Automated Jailbreak Attacks on Large Language Models Utilizing Uncommon Text-Enco
  • WHEN LLM MEETS DRL: ADVANCING JAILBREAKING EFFICIENCY VIA DRL-GUIDED SEARCH
  • Context Injection Attacks on Large Language Models
  • Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens
  • Phantom: General Trigger Attacks on Retrieval Augmented Language Generation
  • On Trojans in Refined Language Models
  • A Survey of Backdoor Attacks and Defenses on Large Language Models: Implications for Security Measur
  • How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States
  • JAILBREAKING AS A REWARD MISSPECIFICATION PROBLEM
  • ObscurePrompt: Jailbreaking Large Language Models via Obscure Inpu
  • ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates
  • Improved Techniques for Optimization-Based Jailbreaking on Large Language Models
  • Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
  • Page 1
  • AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization
  • Chain of Attack: a Semantic-Driven Contextual Multi-Turn attacker for LLM
  • CAN LLMS DEEPLY DETECT COMPLEX MALICIOUS QUERIES? A FRAMEWORK FOR JAILBREAKING VIA OBFUSCATING INTEN
  • SHADOW ALIGNMENT: THE EASE OF SUBVERTING SAFELY-ALIGNED LANGUAGE MODELS
  • Hidden You Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Logic Chai
  • JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models
  • AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbre
  • SANDWICH ATTACK: MULTI-LANGUAGE MIXTURE ADAPTIVE ATTACK ON LLMS
  • Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
  • Using Hallucinations to Bypass RLHF Filters
  • TARGET: Template-Transferable Backdoor Attack Against Prompt-based NLP Models via GPT4
  • SHADOW ALIGNMENT: THE EASE OF SUBVERTING SAFELY-ALIGNED LANGUAGE MODELS
  • OPEN SESAME! UNIVERSAL BLACK BOX JAILBREAKING OF LARGE LANGUAGE MODELS
  • Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
  • Weak-to-Strong Jailbreaking on Large Language Models
  • Punctuation Matters! Stealthy Backdoor Attack for Language Models
  • BYPASSING THE SAFETY TRAINING OF OPEN-SOURCE LLMS WITH PRIMING ATTACKS
  • Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
  • A Semantic, Syntactic, And Context-Aware Natural Language Adversarial Example Generator
  • Fast Adversarial Attacks on Language Models In One GPU Minute
  • Don’t Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models
  • Scaling Behavior of Machine Translation with Large Language Models under Prompt Injection Attacks
  • Automatic and Universal Prompt Injection Attacks against Large Language Models
  • Automatic and Universal Prompt Injection Attacks against Large Language Models
  • Prompt Injection Attacks and Defenses in LLM-Integrated Applications
  • TENSOR TRUST: INTERPRETABLE PROMPT INJECTION ATTACKS FROM AN ONLINE GAME
  • DPP-Based Adversarial Prompt Searching for Lanugage Models
  • Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Agai
  • Rapid Adoption, Hidden Risks: The Dual Impact of Large Language Model Customization
  • Using Hallucinations to Bypass RLHF Filters
  • Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
  • Prompt Injection attack against LLM-integrated Applications
  • Scaling Behavior of Machine Translation with Large Language Models under Prompt Injection Attacks
  • FINE-TUNING ALIGNED LANGUAGE MODELS COMPROMISES SAFETY, EVEN WHEN USERS DO NOT INTEND TO!
  • CATASTROPHIC JAILBREAK OF OPEN-SOURCE LLMS VIA EXPLOITING GENERATION
  • EVALUATING THE SUSCEPTIBILITY OF PRE-TRAINED LANGUAGE MODELS VIA HANDCRAFTED ADVERSARIAL EXAMPLES
  • Defending LLMs against Jailbreaking Attacks via Backtranslation
  • EMULATED DISALIGNMENT: SAFETY ALIGNMENT FOR LARGE LANGUAGE MODELS MAY BACKFIRE!
  • GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
  • ON THE SAFETY OF OPEN-SOURCED LARGE LAN GUAGE MODELS: DOES ALIGNMENT REALLY PREVENT THEM FROM BEING
  • Unveiling the Implicit Toxicity in Large Language Models
  • Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
  • Make Them Spill the Beans! Coercive Knowledge Extraction from (Production) LLMs
  • Learning to Poison Large Language Models During Instruction Tuning
  • ALIGNMENT IS NOT SUFFICIENT TO PREVENT LARGE LANGUAGE MODELS FROM GENERATING HARMFUL IN FORMATION:
  • LANGUAGE MODEL UNALIGNMENT: PARAMETRIC RED-TEAMING TO EXPOSE HIDDEN HARMS AND BI ASES
  • Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
  • IMMUNIZATION AGAINST HARMFUL FINE-TUNING AT TACKS
  • EMULATED DISALIGNMENT: SAFETY ALIGNMENT FOR LARGE LANGUAGE MODELS MAY BACKFIRE!
  • Composite Backdoor Attacks Against Large Language Models
  • AWolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easi
  • ALL IN HOW YOU ASK FOR IT: SIMPLE BLACK-BOX METHOD FOR JAILBREAK ATTACKS
  • LLMJailbreak Attack versus Defense Techniques- A Comprehensive Study
  • Weak-to-Strong Jailbreaking on Large Language Models
  • MULTIVERSE: Exposing Large Language Model Alignment Problems in Diverse Worlds
  • Universal and Transferable Adversarial Attacks on Aligned Language Models
  • COERCING LLMS TO DO AND REVEAL (ALMOST) ANYTHING
  • Generating Valid and Natural Adversarial Examples with Large Language Models
  • Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
  • Scaling Laws for Adversarial Attacks on Language Model Activations
  • Ignore Previous Prompt: Attack Techniques For Language Models
  • ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
  • A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems
  • ATTACKING LARGE LANGUAGE MODELS WITH PROJECTED GRADIENT DESCENT
  • Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack
  • Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embed
  • Query-Based Adversarial Prompt Generation
  • COERCING LLMS TO DO AND REVEAL (ALMOST) ANYTHING
  • Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
  • Fast Adversarial Attacks on Language Models In One GPU Minute
  • DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
  • From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Transla
  • Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
  • CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models
  • Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content
  • Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment
  • PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails
  • A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
  • LoRA-as-an-Attack! Piercing LLM Safety Under The Share-and-Play Scenario
  • Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data
  • SHORTCUTS ARISING FROM CONTRAST: EFFECTIVE AND COVERT CLEAN-LABEL ATTACKS IN PROMPT-BASED LEARNING
  • What’s in Your “Safe” Data?: Identifying Benign Data that Breaks Safety
  • DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
  • Attacking LLM Watermarks by Exploiting Their Strengths
  • From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Transla
  • DeepInception: Hypnotize Large Language Model to Be Jailbreaker
  • Hijacking Large Language Models via Adversarial In-Context Learning
  • EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
  • LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language Models
  • DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
  • Syntactic Ghost: An Imperceptible General-purpose Backdoor Attacks on Pre-trained Language Models
  • Conversation Reconstruction Attack Against GPT Models
  • Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
  • PoisonedRAG: Knowledge Poisoning Attacks to Retrieval-Augmented Generation of Large Language Models
  • COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
  • Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit
  • Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks
  • Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak
  • UNIVERSAL JAILBREAK BACKDOORS FROM POISONED HUMAN FEEDBACK
  • Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking
  • Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation
  • POISONPROMPT: BACKDOOR ATTACK ON PROMPT-BASED LARGE LANGUAGE MODELS
  • BACKDOORING INSTRUCTION-TUNED LARGE LANGUAGE MODELS WITH VIRTUAL PROMPT INJECTION
  • Backdoor Attacks for In-Context Learning with Language Models
  • Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
  • UOR: Universal Backdoor Attacks on Pre-trained Language Models
  • Fake Alignment: Are LLMs Really Aligned Well?
  • Syntactic Ghost: An Imperceptible General-purpose Backdoor Attacks on Pre-trained Language Models
  • Backdoor Activation Attack: Attack Large Language Models using Activation Steering for Safety-Alignm
  • Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control
  • Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Agai
  • Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning
  • BADCHAIN: BACKDOOR CHAIN-OF-THOUGHT PROMPTING FOR LARGE LANGUAGE MODELS
  • AUTODAN: INTERPRETABLE GRADIENT-BASED ADVERSARIAL ATTACKS ON LARGE LANGUAGE MODELS
  • AN LLM CAN FOOL ITSELF: A PROMPT-BASED ADVERSARIAL ATTACK
  • AUTOMATIC HALLUCINATION ASSESSMENT FOR ALIGNED LARGE LANGUAGE MODELS VIA TRANSFERABLE ADVERSARIAL AT
  • LLM LIES: HALLUCINATIONS ARE NOT BUGS, BUT FEATURES AS ADVERSARIAL EXAMPLES
  • LOFT: LOCAL PROXY FINE-TUNING FOR IMPROVING TRANSFERABILITY OF ADVERSARIAL ATTACKS AGAINST LARGE LAN
  • Universal and Transferable Adversarial Attacks on Aligned Language Models
  • Robustness Over Time: Understanding Adversarial Examples’ Effectiveness on Longitudinal Versions of
  • BASELINE DEFENSES FOR ADVERSARIAL ATTACKS AGAINST ALIGNED LANGUAGE MODELS
  • Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue
  • Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Recon
  • Adversarial Demonstration Attacks on Large Language Models
  • COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models
  • The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Mod
  • Open the Pandora’s Box of LLMs: Jailbreaking LLMs through Representation Engineering
  • How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Huma
  • Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models
  • PANDORA: Jailbreak GPTs by Retrieval Augmented Generation Poisoning
  • Weak-to-Strong Jailbreaking on Large Language Models
  • Jailbreaking Proprietary Large Language Models using Word Substitution Cipher
  • Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs
  • Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
  • Jailbroken: How Does LLM Safety Training Fail?
  • ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
  • GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large
  • Tastle: Distract Large Language Models for Automatic Jailbreak Attack
  • Exploring Safety Generalization Challenges of Large Language Models via Code
  • Learning to Poison Large Language Models During Instruction Tuning
  • BADEDIT: BACKDOORING LARGE LANGUAGE MODELS BY MODEL EDITING
  • Composite Backdoor Attacks Against Large Language Models
  • LoRA-as-an-Attack! Piercing LLM Safety Under The Share-and-Play Scenario
  • Stealthy and Persistent Unalignment on Large Language Models via Backdoor Injections
  • ALL IN HOW YOU ASK FOR IT: SIMPLE BLACK-BOX METHOD FOR JAILBREAK ATTACKS
  • THE POISON OF ALIGNMENT
  • The Philosopher’s Stone: Trojaning Plugins of Large Language Models
  • RAPID OPTIMIZATION FOR JAILBREAKING LLMS VIA SUBCONSCIOUS EXPLOITATION AND ECHOPRAXIA
  • Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks
  • RED TEAMING GPT-4V: ARE GPT-4V SAFE AGAINST UNI/MULTI-MODAL JAILBREAK ATTACKS ?
  • PAL: Proxy-Guided Black-Box Attack on Large Language Models
  • INCREASED LLM VULNERABILITIES FROM FINETUNING AND QUANTIZATION
  • Rethinking How to Evaluate Language Model Jailbreak
  • COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability
  • GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation
  • Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation
  • Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs
  • AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
  • Universal Adversarial Triggers Are Not Universal
  • PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails
• LLM-Defense
  • LANGUAGE MODELS ARE HOMER SIMPSON!
  • garak : A Framework for Security Probing Large Language Models
  • Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
  • Trojan Detection in Large Language Models: Insights from The Trojan Detection Challenge
  • PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning
  • The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
  • BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM Safeguards
  • Cross-Task Defense: Instruction-Tuning LLMs for Content Safety
  • Efficient Adversarial Training in LLMs with Continuous Attacks
  • StruQ: Defending Against Prompt Injection with Structured Queries
  • Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning
  • GradSafe: Detecting Unsafe Prompts for LLMs via Safety-Critical Gradient Analysis
  • Defending Jailbreak Prompts via In-Context Adversarial Game
  • Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework
  • Jailbreaker in Jail: Moving Target Defense for Large Language Models
  • DEFENDING AGAINST ALIGNMENT-BREAKING ATTACKS VIA ROBUSTLY ALIGNED LLM
  • Causality Analysis for Evaluating the Security of Large Language Models
  • AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
  • Jailbreaking is Best Solved by Definition
  • RIGORLLM: RESILIENT GUARDRAILS FOR LARGE LANGUAGE MODELS AGAINST UNDESIRED CONTENT
  • LANGUAGE MODELS ARE HOMER SIMPSON! Safety Re-Alignment of Fine-tuned Language Models through Task Ar
  • Defending Against Indirect Prompt Injection Attacks With Spotlighting
  • LLMGuard: Guarding against Unsafe LLM Behavior
  • Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations
  • ON TROJAN SIGNATURES IN LARGE LANGUAGE MODELS OF CODE
  • Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space
  • Detoxifying Large Language Models via Knowledge Editing
  • MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
  • THE POISON OF ALIGNMENT
  • ROSE: Robust Selective Fine-tuning for Pre-trained Language Models
  • GAINING WISDOM FROM SETBACKS : ALIGNING LARGE LANGUAGE MODELS VIA MISTAKE ANALYSIS
  • Making Harmful Behaviors Unlearnable for Large Language Models
  • Fake Alignment: Are LLMs Really Aligned Well?
  • Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
  • Vaccine: Perturbation-aware Alignment for Large Language Model
  • DEFENDING LARGE LANGUAGE MODELS AGAINST JAILBREAK ATTACKS VIA SEMANTIC SMOOTHING
  • Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement
  • DEFENDING AGAINST ALIGNMENT-BREAKING AT TACKS VIA ROBUSTLY ALIGNED LLM
  • LLMSelf Defense: By Self Examination, LLMsKnowTheyAreBeing Tricked
  • BASELINE DEFENSES FOR ADVERSARIAL ATTACKS AGAINST ALIGNED LANGUAGE MODELS
  • Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment
  • LLMsCanDefend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper
  • Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts
  • Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models
  • Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Lang
  • CAMOUFLAGE IS ALL YOU NEED: EVALUATING AND ENHANCING LANGUAGE MODEL ROBUSTNESS AGAINST CAMOUFLAGE AD
  • Defending Jailbreak Prompts via In-Context Adversarial Game
  • Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement
  • Defending LLMs against Jailbreaking Attacks via Backtranslation
  • IMMUNIZATION AGAINST HARMFUL FINE-TUNING ATTACKS
  • Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield
  • JAB: Joint Adversarial Prompting and Belief Augmentation
  • TOKEN-LEVEL ADVERSARIAL PROMPT DETECTION BASED ON PERPLEXITY MEASURES AND CONTEXTUAL INFORMATION
  • Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
  • Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning
  • Vaccine: Perturbation-aware Alignment for Large Language Model
  • Improving the Robustness of Large Language Models via Consistency Alignment
  • SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding
  • Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization
  • Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
  • LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors
  • Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language
  • Analyzing And Editing Inner Mechanisms of Backdoored Language Models
  • Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
  • ROBUSTIFYING LANGUAGE MODELS WITH TESTTIME ADAPTATION
  • Jailbreaker in Jail: Moving Target Defense for Large Language
  • DETECTING LANGUAGE MODEL ATTACKS WITH PERPLEXITY
  • Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation an
  • From Adversarial Arms Race to Model-centric Evaluation Motivating a Unified Automatic Robustness Eva
  • LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
  • Intention Analysis Makes LLMs A Good Jailbreak Defender
  • Defending Against Disinformation Attacks in Open-Domain Question Answering
  • Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning
  • Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landsc
  • Round Trip Translation Defence against Large Language Model Jailbreaking Attacks
  • How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
  • SELF-GUARD: Empower the LLM to Safeguard Itself
  • Intention Analysis Makes LLMs A Good Jailbreak Defender
  • Jatmo: Prompt Injection Defense by Task-Specific Finetuning
  • Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
  • Adversarial Text Purification: A Large Language Model Approach for Defense
  • Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning
  • Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
  • Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
  • Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Application
  • Is the System Message Really Important to Jailbreaks in Large Language Models?
  • AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts
  • Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge