5 papers
Encrypted Neural Networks without Overflows
Philipp Kern, Lorenzo Rovida, Samuel Teuber +3
The popular Cheon-Kim-Kim-Song (CKKS) scheme enables efficient private inference in neural networks by evaluating them on encrypted data. Since CKKS only supports addition, multipl…
The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results
Konstantin Kaulen, Tobias Ladner, Stanley Bak +8
This report summarizes the 6th International Verification of Neural Networks Competition (VNN-COMP 2025), held as a part of the 8th International Symposium on AI Verification (SAIV…
GPT, But Backwards: Exactly Inverting Language Model Outputs
Adrians Skapars, Edoardo Manino, Youcheng Sun +1
The task of reconstructing unknown textual inputs to language models is a fundamental auditing primitive that allows us to assess the model's vulnerability to a range of security i…
Floating-Point Neural Network Verification at the Software Level
Edoardo Manino, Bruno Farias, Rafael Sá Menezes +3
The behaviour of neural network components must be proven correct before deployment in safety-critical systems. Unfortunately, existing neural network verification techniques canno…
Neural Network Verification is a Programming Language Challenge
Lucas C. Cordeiro, Matthew L. Daggitt, Julien Girard-Satabin +8
Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while pr…