Cover Image

Towards Safeguarding LLM Fine-tuning APIs against Cipher Attacks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Youstra, Jack, Mahfoud, Mohammed, Yan, Yang, Sleight, Henry, Perez, Ethan, Sharma, Mrinank
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language
by: Guo, Shiyuan, et al.
Published: (2025)

Forecasting Rare Language Model Behaviors
by: Jones, Erik, et al.
Published: (2025)

Rapid Response: Mitigating LLM Jailbreaks with a Few Examples
by: Peng, Alwin, et al.
Published: (2024)

Eliciting Harmful Capabilities by Fine-Tuning On Safeguarded Outputs
by: Kaunismaa, Jackson, et al.
Published: (2026)

Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment
by: Wang, Haozhong, et al.
Published: (2026)

Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024)

Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach
by: Wang, Tony T., et al.
Published: (2024)

The LLM Has Left The Chat: Evidence of Bail Preferences in Large Language Models
by: Ensign, Danielle, et al.
Published: (2025)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
by: Zhou, Huichi, et al.
Published: (2025)

Safeguarding Graph Neural Networks against Topology Inference Attacks
by: Fu, Jie, et al.
Published: (2025)

Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
by: Huang, Tiansheng, et al.
Published: (2024)

Persona Vectors: Monitoring and Controlling Character Traits in Language Models
by: Chen, Runjin, et al.
Published: (2025)

Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
by: Huang, Tiansheng, et al.
Published: (2024)

MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs
by: Chen, Boyuan, et al.
Published: (2025)

Log Probability Tracking of LLM APIs
by: Chauvin, Timothée, et al.
Published: (2025)

Analyzing the Effect of Noise in LLM Fine-tuning
by: Li, Lingfang, et al.
Published: (2026)

GuardReasoner: Towards Reasoning-based LLM Safeguards
by: Liu, Yue, et al.
Published: (2025)

Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?
by: Ma, Yuhang, et al.
Published: (2026)

Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks
by: Poppi, Samuele, et al.
Published: (2024)

Adversarial Inception Backdoor Attacks against Reinforcement Learning
by: Rathbun, Ethan, et al.
Published: (2024)

Incorporating Unlabelled Data into Bayesian Neural Networks
by: Sharma, Mrinank, et al.
Published: (2023)

Token-Efficient Change Detection in LLM APIs
by: Chauvin, Timothée, et al.
Published: (2026)

Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
by: Zhang, Jiale, et al.
Published: (2025)

Towards Fast LLM Fine-tuning through Zeroth-Order Optimization with Projected Gradient-Aligned Perturbations
by: Mi, Zhendong, et al.
Published: (2025)

Hierarchical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM
by: Yao, Yongqiang, et al.
Published: (2025)

Detecting Instruction Fine-tuning Attacks using Influence Function
by: Li, Jiawei
Published: (2025)

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
by: Fu, Wenjie, et al.
Published: (2023)

Deep Learning-Assisted Improved Differential Fault Attacks on Lightweight Stream Ciphers
by: Lim, Kok Ping, et al.
Published: (2026)

Fundamental Limitations in Pointwise Defences of LLM Finetuning APIs
by: Davies, Xander, et al.
Published: (2025)

Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
by: Ding, Meng, et al.
Published: (2024)

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?
by: Du, Hao, et al.
Published: (2025)

Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
by: Schaeffer, Rylan, et al.
Published: (2024)

Membership Inference Attacks Against Fine-tuned Diffusion Language Models
by: Chen, Yuetian, et al.
Published: (2026)

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
by: Wang, Yi-Siang, et al.
Published: (2026)

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
by: Kim, Minseon, et al.
Published: (2025)

HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization
by: Su, Yang, et al.
Published: (2024)

Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning
by: Li, Boheng, et al.
Published: (2025)

LoRA vs Full Fine-tuning: An Illusion of Equivalence
by: Shuttleworth, Reece, et al.
Published: (2024)

Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
by: Hsiung, Lei, et al.
Published: (2025)

Attacks and Defenses Against LLM Fingerprinting
by: Kurian, Kevin, et al.
Published: (2025)