PentAGI

Local LLMs in AppSec: What Open Tools and Models Can Handle in Application Security

The article examines how local large language models can be integrated into application security workflows without relying on cloud subscriptions. It traces the evolution from llama.cpp in 2023 through agentic frameworks like PentAGI and Strix in 2025-2026. Teams can now run models on local hardware, connect them to code analyzers such as Semgrep, and delegate specific tasks like vulnerability path reconstruction or multi-step application testing. The piece highlights practical scenarios including static code review, dynamic testing with browser and terminal agents, and security evaluation of embedded AI assistants using tools like Garak and PyRIT. It stresses the importance of task decomposition, context management, and verification of model outputs to avoid misleading results. Hardware performance, model selection, and custom agent configurations are presented as key factors for successful adoption.

CyberXero Deploys 51 AI Agents to Scan 4708 Targets and Implant 32 WebShells in 61 Seconds

Russian-speaking initial access broker CyberXero integrated Anthropic's Claude Code and the open-source PentAGI autonomous penetration agent into its attack pipeline, creating a cluster of 51 specialized AI agents. The group ran two parallel operations: one automated campaign targeting vulnerable WordPress and e-commerce sites worldwide using known exploits including CVE-2026-63030, CVE-2026-60137 and CVE-2026-4815, and a second, more targeted campaign against Ukrainian energy and critical infrastructure. In one recorded execution the AI cluster scanned 4708 public-facing hosts, compromised 429 WordPress administrative panels and dropped 32 WebShells within a 61-second window. Attackers bypassed model safety guardrails by resetting sessions and reframing requests as authorized penetration tests of their own infrastructure. The operation relied on an eight-node architecture that included a Linux workstation, configuration server, payload staging server and public Redis instances, two of which belonged to Chinese organizations. SOCRadar warns that session-level controls alone are insufficient and recommends enforcing system-level boundaries, minimal privileges and strict egress filtering for any AI agents.