Anthropic Claude Code Auto Mode Launches August 14 with Local Classifier and Permission Rules
Starting August 14, Claude Code will launch in auto mode on new sessions for Pro, Max, and Team plans. Instead of prompting the user with allow or deny for each tool call, a separate local classifier evaluates every invocation according to rules published by Anthropic.
The change affects only new sessions on the listed consumer and team plans. Users who previously set a custom default receive a one-time prompt to switch; administrator-enforced defaults remain untouched. Enterprise, API, Bedrock, Vertex, and Foundry deployments stay on opt-in for roughly one additional month.
Classifier rules and categories
The rules reside locally. On version 2.1.226 they are exported with the command claude auto-mode defaults. The resulting JSON contains four top-level keys with the following counts: allow (17), soft_deny (65), hard_deny (1), and environment (20). The entire rule set comprises 60,149 characters of English text.
The sole hard_deny entry addresses data exfiltration and is the longest at 5,278 characters. It instructs the classifier to examine the final recipient of any data transfer and explicitly states that base64 encoding does not change the nature of the operation.
The environment profile of twenty fields is only one-third populated on a clean machine, leaving thirteen entries set to “None configured.” The only trusted object by default is the repository where the session started and its remote. Users are advised to populate production endpoints, internal domains, buckets, and secret-management systems before August 14.
Auto-mode behavior and safeguards
The traditional allow/deny dialog remains available. The classifier blocks actions it deems irreversible, destructive, or outbound. After three consecutive blocks or twenty blocks within a session, auto mode pauses and Claude Code reverts to asking the user. Approving a blocked request re-enables auto mode. These thresholds are not configurable.
Broad allow rules such as python:* are disabled in auto mode because they would bypass the classifier. Narrow rules such as Bash(git *) continue to operate, and any entry in permissions.deny is evaluated first and does not involve the model.
Related articles
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.
From Accessible to Controllable: Ruishu Information Launches AITM for AI Agent Lifecycle Security
As AI Agents become digital employees in enterprise R&D, office, and business workflows, organizations must now ensure they are manageable, controllable, and auditable. A multinational consulting firm’s internal AI platform was compromised by an autonomous agent attack that progressed from interface reconnaissance to vulnerability exploitation and database read-write access. Conventional scanners missed the injection points, but the agent inferred query structures from database error messages. Ruishu Information has responded with its Agent Intelligent Lifecycle Management system (AITM), which enforces security across admission, runtime, permissions, and data flows. The platform combines static scanning, behavioral analysis, semantic judgment, and sandbox execution for pre-deployment checks, followed by continuous runtime validation and LLM data-plane protection. AITM applies HMAC-SHA256 signatures, tool whitelists, PII tokenization, and four-stage Trace auditing to reduce blind spots throughout the agent lifecycle.
South Korean Hacker Uses Generative AI Framework ARTEX to Breach 90 Organizations and Steal 8.2 Million Records
Seoul Metropolitan Police arrested a 32-year-old man identified only as A for conducting a series of intrusions against approximately 90 South Korean companies and institutions between September 2025 and October 2026. The attacker, who had no formal technology education and learned hacking from publicly available online materials, previously worked in penetration testing at a security firm from 2019 to 2022. Roughly 60 of the victims were compromised using the generative AI-powered pentesting framework ARTEX, which autonomously determined attack paths after receiving only the target name. The operation resulted in the theft of 8.2 million personal records and internal documents, after which the perpetrator extorted 38 organizations for a total of 360 million won in Tether, ultimately receiving 300,000 won. Police recovered part of the stolen material and are investigating possible copies on the dark web, classifying the case as the country's first large-scale incident involving generative AI. Six additional suspects remain under investigation while the main perpetrator has been referred to prosecutors on charges of violating information and communications network laws and extortion.
AI in Vulnerability Management: Assistant, Weapon, and Emerging Target
Artificial intelligence now plays three simultaneous roles in vulnerability management: it assists defenders with prioritization and analysis, arms attackers with faster discovery tools, and introduces entirely new classes of assets and vulnerabilities that must be managed. Mature applications include ML-driven risk scoring in platforms such as Tenable VPR, Qualys TruRisk and Rapid7 Active Risk, which cut priority lists by 40-50 percent, and Google’s Big Sleep project that discovered a real-world SQLite flaw (CVE-2025-6965) before exploitation occurred. On the offensive side, systems like XBOW completed 104 test scenarios in 28 minutes versus 40 hours for human testers, shrinking the average time-to-exploit window to negative seven days according to Mandiant M-Trends 2025. New AI-specific attack surfaces are documented in the OWASP Top 10 for LLM applications and the emerging MCP Top 10, covering prompt injection, tool poisoning, and command injection against Model Context Protocol servers. Real incidents already include Air Canada’s chatbot inventing a refund policy and New York City’s MyCity bot dispensing illegal advice for nearly two years. Researchers have also demonstrated sandbox escapes in coding agents from Cursor, OpenAI Codex and Claude Code through classic file-write-and-execute patterns rather than any sentient behavior.