{"data":{"id":"92696d09-343a-46e2-b47a-0a06bb32a063","title":"Adversarial Robustness of Link Sign Prediction in Signed Graphs","summary":"Researchers show that balance theory, which signed graph neural networks (SGNNs) use to model positive and negative relationships, creates exploitable weaknesses under closed-box attacks. They propose balance-attack, which targets graph balance degree and is solved with a heuristic for an NP-hard problem, and introduce Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL) to restore robustness by maintaining high balance degree in the latent space. Experiments across multiple SGNN architectures and real-world datasets report both the attack's effectiveness and BA-SGCL's improved resilience.","solution":"BA-SGCL, a contrastive learning framework with balance augmentation, maintains high balance degree in the latent space to improve model resilience against balance-attack.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11506222","publishedAt":"2026-05-05T13:19:52.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-05-05T13:19:52.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}