{"total":14,"items":[{"citing_arxiv_id":"2605.13210","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PoisonCap: Efficient Hierarchical Temporal Safety for CHERI","primary_cat":"cs.AR","submitted_at":"2026-05-13T08:59:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PoisonCap uses a new poison capability format to deliver strict use-after-free and initialization safety for CHERI systems with no fundamental overhead over Cornucopia baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09961","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Towards LLM-Based Analysis of Virtualization-Obfuscated Code through Automated Data Generation","primary_cat":"cs.CR","submitted_at":"2026-05-11T04:15:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An automated static-analysis pipeline generates labeled structural units from virtualization-obfuscated binaries so LLMs can analyze them without exceeding token limits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09203","ref_index":26,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Removing the Watermark Is Not Enough: Forensic Stealth in Generative-AI Watermark Removal","primary_cat":"cs.CR","submitted_at":"2026-05-09T22:45:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Current AI image watermark removal attacks replace the watermark with a different forensic signal, allowing independent detectors to distinguish processed outputs from clean images at over 98% true-positive rate under a 1% false-positive budget.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08316","ref_index":90,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey","primary_cat":"cs.CR","submitted_at":"2026-05-08T14:58:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A literature survey synthesizes 119 studies on AI-driven alert screening into a four-stage taxonomy of filtering, triage, correlation, and generative augmentation while identifying gaps in deployment realism and robustness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07812","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification","primary_cat":"cs.CR","submitted_at":"2026-05-08T14:45:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07008","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pomegranate: A Lightweight Compartmentalization Architecture using Virtualization Extensions","primary_cat":"cs.CR","submitted_at":"2026-05-07T22:44:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Pomegranate compartmentalizes commodity OS kernels via virtualization extensions, sentry functions, and EPT-enforced policies, achieving negligible overhead on a Linux network stack when compartment boundaries limit cross-talk.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04491","ref_index":110,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An Evaluation of Chat Safety Moderations in Roblox","primary_cat":"cs.CY","submitted_at":"2026-05-06T04:41:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Roblox's automated chat moderation fails to catch numerous unsafe messages involving grooming, sexualization of minors, bullying, violence, self-harm, and sensitive information sharing, with users evading detection through various techniques.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01025","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools","primary_cat":"cs.GT","submitted_at":"2026-05-01T18:37:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00314","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Semia: Auditing Agent Skills via Constraint-Guided Representation Synthesis","primary_cat":"cs.CR","submitted_at":"2026-05-01T00:48:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Semia synthesizes Datalog representations of agent skills via constraint-guided loops to enable reachability queries for semantic risks, finding critical issues in over half of 13,728 real skills with 97.7% recall on expert-labeled samples.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21700","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers","primary_cat":"cs.CR","submitted_at":"2026-04-23T14:08:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BadStyle creates stealthy backdoors in LLMs by poisoning samples with imperceptible style triggers and using an auxiliary loss to stabilize payload injection, achieving high attack success rates across multiple models while evading defenses.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21169","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Position Paper: Denial-of-Service against Multi-Round Transaction Simulation","primary_cat":"cs.CR","submitted_at":"2026-04-23T00:25:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper examines denial-of-service risks to multi-round transaction simulation arising from inter-transaction dependencies in smart-contract state.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14360","ref_index":90,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Digital Guardians: The Past and The Future of Cyber-Physical Resilience","primary_cat":"cs.CR","submitted_at":"2026-04-15T19:23:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A survey frames CPS resilience through five themes and illustrates them in connected transportation and medical systems to provide a roadmap for real-world resilience.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07493","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Differentially Private Modeling of Disease Transmission within Human Contact Networks","primary_cat":"cs.CR","submitted_at":"2026-04-08T18:34:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A differentially private pipeline using node-level DP summaries to fit ERGMs or SBMs, generate synthetic networks, and simulate SIS disease spread on ARTNet sexual contact data produces incidence, prevalence, and intervention effect sizes close to non-private versions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05461","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Content Fuzzing for Escaping Information Cocoons on Digital Social Media","primary_cat":"cs.CL","submitted_at":"2026-04-07T05:49:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ContentFuzz rewrites posts with LLM guidance from stance model confidence to flip machine labels without altering human intent, tested across four models and three datasets in two languages.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}