Container escape attacks break isolation boundaries, granting threat actors code execution on the underlying host and potentially full control over the entire cluster. Existing runtime defenses exhibit an inherent trade-off. Anomaly- and provenance-based detection mechanisms achieve broad escape detection, yet incur substantial operational costs due to model retraining requirements or system-wide provenance capture. In contrast, industry rule-based detectors avoid these costs but offer limited detection coverage.

Privacy-preserving biometric template protection schemes (BTPs) preserve biometric data by hiding biometric representations via a privacy-preserving mechanism (such as homomorphic encryption) and comparing the protected templates while conserving the recognition scores as in an embedding space. However, it is often tolerated to reveal these scores after performing a biometric comparison to gain efficiency and perform the score comparison directly on cleartext data. Through this work, we demonstrate that this cleartext score tolerance can lead to privacy breaches and bypass recognition systems, threatening those BTPs in the case of inner product-based facial template comparisons. We propose a template recovery attack that requires no training and a few random fake templates with their corresponding scores, from which we are able to recover the unprotected target template using the Lagrange multiplier optimization method. We evaluate our attack by verifying whether the recovered template is deemed similar to the target template held by recognition systems set to accept 0.1%, 0.01%, and 0.001% FMR. We estimate that between 60 to 165 revealed scores and fake templates can lead to a template recovery with a 100% success rate. We analyzed the impact of recovered templates by measuring the amount of gender information they contain, as well as their resemblance to the reconstructed images of their target templates.

The cutting-edge biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table-lookups and summation only. We study quantization parameters for the values in the lookup tables and evaluate performances on both synthetic and facial feature vectors for which we achieve a recognition performance identical to the non-tabularized baseline systems. We then assess their efficiency under HE and record runtimes between 28.95ms and 59.35ms for the three security levels, demonstrating their enhanced speed.

Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. Several solutions have been developed to protect the sensitive biometric data in such systems that provide security against honest-but-curious (a.k.a. semi-honest) attackers. However, in practice, attackers typically do not act honestly and multiple studies have shown severe biometric information leakage in such honest-but-curious solutions when considering dishonest, malicious attackers. In this paper, we propose a provably secure biometric verification protocol to withstand malicious attackers and prevent biometric data from any leakage. The proposed protocol is based on a homomorphically encrypted log likelihood-ratio (HELR) classifier that supports any biometric modality (e.g., face, fingerprint, dynamic signature, etc.) encoded as a fixed-length real-valued feature vector. The HELR classifier performs an accurate and fast biometric recognition. Furthermore, our protocol, which is secure against malicious adversaries, is designed from a protocol secure against semi-honest adversaries enhanced by zero-knowledge proofs. We evaluate both protocols for various security levels and record a sub-second speed (between 0.37s and 0.88s) for the protocol secure against semi-honest adversaries and between 0.95s and 2.50s for the protocol secure against malicious adversaries.