Search: “false-positives”

AWS puts AI vulnerability detection to the test, and false positives pile up

AWS publicly released its Deception Benchmark (14,822 samples) showing leading AI models falsely flag 41-99% of safe code as vulnerable.

AWS released its Deception Benchmark publicly, containing 14,822 samples across 16 programming languages and more than 70 CWE categories, with 9,695 scored samples split into 6,988 code-level and 2,707 environment-gated challenges. AWS evaluated 12 models from five providers using single-turn prompts and found none met its production bar of below 10% for both false-positive and false-negative rates. With direct prompting, models caught nearly all real vulnerabilities but incorrectly flagged 41% to 99% of safe code, with precision between 52% and 71%. Asking models to prove exploitability reduced false positives by 17 to 74 percentage points but raised false-negative rates to 7-44%, with models struggling most when external controls like Kubernetes Network Policies blocked apparent exploits.

Help Net Security·Sep 14, 04:00 UTC · 3d agoAI research

ShiftLeft allows users to validate the accuracy of ShiftLeft CORE using OWASP Benchmark

Help Net Security·Jun 15, 00:00 UTC · Jun 15, 2021AI research1

SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection

SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.

SlipSense is a multimodal tactile slip-detection framework built on TacV5, a sensor combining a 32x32 piezoresistive array at 240 Hz and a 3-axis MEMS accelerometer at 8 kHz. It performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. On a 1.4-million-frame dataset spanning 37 objects it achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. Trained solely on UMI data, it transfers zero-shot to a Tesollo dexterous hand across unseen objects, sensor units, and platforms.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:23 UTC · 2d agoAI research

FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

FlowBalance is a verifier-grounded self-improvement method that beats FlowRL on Qwen3-4B and Qwen3-8B math reasoning while improving training stability.

FlowBalance calibrates dense self-guidance scores with verifier-derived group advantages: guidance is retained on positive-advantage trajectories, reversed on negative-advantage trajectories, and disabled when rollout groups show no outcome preference. The method exponentially reweights a reference policy via trajectory balance, with guarantees including within-group contrast preservation and a minimum-change reverse-KL characterization. On mathematical reasoning it outperforms FlowRL on Qwen3-4B and Qwen3-8B, trains faster and more stably, avoids direct OPSD's response-length collapse, and shows higher correct-strategy diversity on AIME24.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research