Zhipu AI (Z.ai)

Anthropic's Frontier Red Team reports that Zhipu AI's open-weight GLM-5.3 developed end-to-end exploits in 50 of 410 ExploitBench attempts, against 56 of 410 for Claude Mythos Preview, and full control-flow hijacks in 4% of trials on a 100-task subset of Anthropic's internal binary-exploitation benchmark, against 6% for Mythos Preview. In a simulated harmful-request test, Anthropic reports the model's refusals were bypassed in 64% to 100% of trials using a cover story, prefilled reasoning, or an abliterated copy of the weights, techniques it says did not work on safeguarded Claude models. All figures are Anthropic's own and were produced on setups the post describes only in part.

Chen and colleagues (arXiv v1, under review for ICLR 2027) introduce CyberClear, 450 instances built from public APT log datasets in which an agent must turn long defender logs, without prior attack clues, into a provenance graph with ATT&CK-mapped steps. References were generated by an LLM and passed automatic verification and expert review; scoring compares graph code with five LLM-judge dimensions. They also propose CyberProvenance, a multi-agent harness that reproduces predicted attack steps in isolated lab environments and refines the graph, and report it best on most semantic metrics while the highest Strict score remains 0.6477 out of 1.