57 citations · 103 across the 16 of their papers we have counts for
11 papers · 1 filter
Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment
Lipeng He, Yihan Wang, Jiawen Zhang +1
Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses…
SoK: Colluding Adversaries in Machine Learning Pipelines
Vasisht Duddu, Lipeng He, Asim Waheed +1
Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objectives, knowledge, and capabili…
Locket: Robust Feature-Locking Technique for Language Models
Lipeng He, Vasisht Duddu, N. Asokan
Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for free users and advanced models to payi…
PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing
Anthony Hughes, Vasisht Duddu, N. Asokan +2
Language models (LMs) may memorize personally identifiable information (PII) from training data, enabling adversaries to extract it during inference. Existing defense mechanisms su…
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Asim Waheed, Vasisht Duddu, Sebastian Szyller
Machine learning (ML) models are susceptible to various risks to security, privacy, and fairness. Most defenses are designed to protect against each risk individually (intended int…
Combining Machine Learning Defenses without Conflicts
Vasisht Duddu, Rui Zhang, N. Asokan
Machine learning (ML) defenses protect against various risks to security, privacy, and fairness. Real-life models need simultaneous protection against multiple different risks whic…
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations
Vasisht Duddu, Oskari Järvinen, Lachlan J Gunn +1
Regulations increasingly call for various assurances from machine learning (ML) model providers about their training data, training process, and model behavior. For better transpar…
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu, Sebastian Szyller, N. Asokan
Machine learning (ML) models cannot neglect risks to security, privacy, and fairness. Several defenses have been proposed to mitigate such risks. When a defense is effective in mit…
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning
Vasisht Duddu, Sebastian Szyller, N. Asokan
Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an…
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal +1
Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute intellectual property (I…
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal +1
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantag…