NeurIPS 2025PastLarge language models
Lock-LLM Workshop: Prevent Unauthorized Knowledge Use from Large Language Models
NeurIPS Lock-LLM Workshop 2025
- Submission deadline
- Sep 18, 2025, 23:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (57)
Fetched from OpenReview (v2) on 2026-06-10.
A Granular Study of Safety Pretraining under Model Abliteration
AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
ARIAL: An Agentic Framework for Document VQA with Precise Answer Localization
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
Breaking Bad: Interpretability-Based Safety Audits of State-of-the-Art LLMs
Breaking Distortion-free Watermarks in Large Language Models
Can Editing LLMs Inject Harm?
Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning
Compressed but Compromised? A Study of Jailbreaking in Compressed LLMs
Context-Masked Meta-Prompting for Privacy-Preserving LLM Adaptation in Finance
Cross-Modal Attention Guided Unlearning in Vision-Language Models
Cryptographic Fingerprinting for Medical AI: A Proof-of-Concept Approach to Protecting Healthcare ML Models from API Extraction
Differentially Private In-Context Learning with Nearest Neighbor Search
DistilLock: Safeguarding LLMs from Unauthorized Knowledge Distillation on the Edge
Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Does Machine Unlearning Truly Remove Knowledge?
DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation
Economic Confidentiality without Secrets: Making Intercepted LLM-Agent Communications Unusable
Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?
Evaluating and Mitigating Contextual Vulnerabilities in LLMs: An Architectural Approach to Resisting Multi-Turn Jailbreaks
Evaluating Privacy Leakage From In-Context Learning
Exploiting the Experts: Unauthorized Compression in MoE-LLMs
How to Make LLMs Safer? Detecting and Editing Key Heads in LLMs
Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?
Jailbreak Distillation: Renewable Safety Benchmarking
Key-Conditioned Orthonormal Transform Gating (K-OTG): Multi-Key Access Control with Hidden-State Scrambling for LoRA-Tuned Models
LLMs can hide text in other text of the same length
LSMAS (LLM Security Modeling via Activation Steering)
MacroBench: A Novel Testbed for Web Automation Scripts via Large Language Models
MarkTune: Advancing the Quality-Detectability Pareto Frontier of Open-Weight LM Watermarking
MGA-VQA: Secure and Interpretable Graph-Augmented Visual Question Answering with Memory-Guided Protection Against Unauthorized Knowledge Use
Model Immunization by Trapping Harmful Finetuning
No Question, No Passage, No Problem: Investigating Artifact Exploitation and Reasoning in Multiple-Choice Reading Comprehension
OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution
On the Relationship Between Neural Tangent Kernel Frobenius Distance and Distillation Sample Complexity
PASTRAL: Privacy-aware AST and TRansformer-based Anomalous command-Line detection
Permissioned LLMs: Enforcing Access Control in Large Language Models
Probe-Rewrite-Evaluate: A Workflow for Reliable Benchmarks and Quantifying Evaluation Awareness
Reasoning Models Can be Easily Hacked by Fake Reasoning Bias
SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning
Safety Subspaces are Not Distinct: A Fine-Tuning Case Study
Scalable Fingerprinting of Large Language Models
SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From
Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption
Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security
The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models
Towards Controlled LLM Unlearning
Towards Quantization-Adversarial Reparameterizations
Towards Realistic Guarantees: A Probabilistic Certificate for SmoothLLM
Un-Distillable LLMs via Entropy-Perturbed Logits
Undistillable Open Language Models with Teacher Scrambling
Unlearners Can Lie: Evaluating “Honesty” in LLM Unlearning
User Confidence-Fueled Stereotypes: Investigating Sycophantic Amplification of Implicit Bias in Language Models
Who’s Your Judge? On the Detectability of LLM-Generated Judgments
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning
X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates
Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs