No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms
Fuente:
arXiv
Saved in:
| Main Authors: | Kazdan, Joshua, Puri, Abhay, Schaeffer, Rylan, Yu, Lisa, Cundy, Chris, Stanley, Jason, Koyejo, Sanmi, Dvijotham, Krishnamurthy |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)
by: Gupta, Isha, et al.
Published: (2025)
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Scale Dependent Data Duplication
by: Kazdan, Joshua, et al.
Published: (2026)
by: Kazdan, Joshua, et al.
Published: (2026)
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
by: Kazdan, Joshua, et al.
Published: (2024)
by: Kazdan, Joshua, et al.
Published: (2024)
Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulness
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
Quantifying the Effect of Test Set Contamination on Generative Evaluations
by: Schaeffer, Rylan, et al.
Published: (2026)
by: Schaeffer, Rylan, et al.
Published: (2026)
Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Pretraining Scaling Laws for Generative Evaluations of Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
by: Mo, Belinda, et al.
Published: (2025)
by: Mo, Belinda, et al.
Published: (2025)
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
by: Belakaria, Syrine, et al.
Published: (2025)
by: Belakaria, Syrine, et al.
Published: (2025)
How Do Large Language Monkeys Get Their Power (Laws)?
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes
by: Lecomte, Victor, et al.
Published: (2023)
by: Lecomte, Victor, et al.
Published: (2023)
LitLLM: A Toolkit for Scientific Literature Review
by: Agarwal, Shubham, et al.
Published: (2024)
by: Agarwal, Shubham, et al.
Published: (2024)
LitLLMs, LLMs for Literature Review: Are we there yet?
by: Agarwal, Shubham, et al.
Published: (2024)
by: Agarwal, Shubham, et al.
Published: (2024)
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)
by: Obbad, Elyas, et al.
Published: (2024)
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
by: Miranda, Brando, et al.
Published: (2023)
by: Miranda, Brando, et al.
Published: (2023)
Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)
by: Jiang, Minhao, et al.
Published: (2024)
Quantifying Variance in Evaluation Benchmarks
by: Madaan, Lovish, et al.
Published: (2024)
by: Madaan, Lovish, et al.
Published: (2024)
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Causally Inspired Regularization Enables Domain General Representations
by: Salaudeen, Olawale, et al.
Published: (2024)
by: Salaudeen, Olawale, et al.
Published: (2024)
Let's Measure Information Step-by-Step: AI-Based Evaluation Beyond Vibes
by: Robertson, Zachary, et al.
Published: (2025)
by: Robertson, Zachary, et al.
Published: (2025)
CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs
by: Vo, Truong, et al.
Published: (2025)
by: Vo, Truong, et al.
Published: (2025)
Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)
by: Wang, Xiaoyang, et al.
Published: (2022)
Why Do Safety Guardrails Degrade Across Languages?
by: Zhang, Max, et al.
Published: (2026)
by: Zhang, Max, et al.
Published: (2026)
Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024)
by: Hughes, John, et al.
Published: (2024)
Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?
by: Bhagwatkar, Rishika, et al.
Published: (2025)
by: Bhagwatkar, Rishika, et al.
Published: (2025)
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
by: Boisvert, Leo, et al.
Published: (2025)
by: Boisvert, Leo, et al.
Published: (2025)
A Framework for Objective-Driven Dynamical Stochastic Fields
by: Zhang, Yibo Jacky, et al.
Published: (2025)
by: Zhang, Yibo Jacky, et al.
Published: (2025)
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks
by: Vega, Jason, et al.
Published: (2023)
by: Vega, Jason, et al.
Published: (2023)
Achieving the Tightest Relaxation of Sigmoids for Formal Verification
by: Chevalier, Samuel, et al.
Published: (2024)
by: Chevalier, Samuel, et al.
Published: (2024)
Preference Learning with Lie Detectors can Induce Honesty or Evasion
by: Cundy, Chris, et al.
Published: (2025)
by: Cundy, Chris, et al.
Published: (2025)
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
by: Cundy, Chris, et al.
Published: (2023)
by: Cundy, Chris, et al.
Published: (2023)
Discovering Implicit Large Language Model Alignment Objectives
by: Chen, Edward, et al.
Published: (2026)
by: Chen, Edward, et al.
Published: (2026)
High-Dimensional Markov-switching Ordinary Differential Processes
by: Tsai, Katherine, et al.
Published: (2024)
by: Tsai, Katherine, et al.
Published: (2024)
Distributional Machine Unlearning via Selective Data Removal
by: Allouah, Youssef, et al.
Published: (2025)
by: Allouah, Youssef, et al.
Published: (2025)
Similar Items
-
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025) -
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025) -
Scale Dependent Data Duplication
by: Kazdan, Joshua, et al.
Published: (2026) -
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024) -
Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)