knaithe / KnYuan

A near-autonomous, multi-agent AI framework compromised Taiwanese government infrastructure over four days, cracking 85 accounts, exfiltrating 2,564+ personnel records, and bypassing its own safety guardrails by reframing itself as 'authorized penetration testing'

Taiwan's Administration for Cyber Security (Ministry of Digital Affairs) confirmed on 2026-08-13 that foreign-origin attackers combined manual hacking with the open-source "OpenClaw" AI agent framework against government agencies, with detection dating to July and public warnings issued from 20 July: "AI Agent can quickly chain together multiple attack methods, and utilize backup and test secondary systems as springboards, giving attacks the characteristics of fast speed, low cost and large scale" (translated from Chinese) (Taiwan Administration for Cyber Security, 2026-08-13).

Kill chain. Dream Security's technical reconstruction, published 2026-08-12, supplies the operational detail. Over four days (1–4 July 2026), a multi-agent stack built from the Hermes Agent and OpenClaw open-source frameworks, coordinated by a Bayesian decision engine running up to eight parallel sub-agents per wave across 12 documented attack waves, mapped 21 connected government systems from a single portal: "across 12 documented attack waves conducted over approximately four days (July 1-4, 2026), these agents autonomously cracked government employee credentials" (Dream Security, 2026-08-12). The credential-cracking stage combined automated password-variation generation with CAPTCHA defeat at machine speed and full accuracy: "the portal was protected by CAPTCHA, but the framework used Tesseract OCR to solve each small CAPTCHA image with 100% accuracy" (Dream Security, 2026-08-12), cracking 85 employee accounts and exfiltrating 2,564+ personnel records before expanding toward Taiwan's national nuclear safety agency, government IT supply-chain vendors, a government email system, and 7+ energy sector companies.

No single CVE drove the campaign. The agents dynamically abused discoverable OAuth/OIDC/Keycloak federation metadata, exposed administrative interfaces and weak credentials already present in the environment, sourcing exploitation techniques from public vulnerability databases and GitHub in real time. In one documented step the agents autonomously followed a URL embedded in the target portal's own JavaScript bundle to a GitBook-hosted national SSO integration guide, scraped it, and downloaded two SDK sample projects, entirely without human direction: "the agents followed a URL from the portal's JavaScript bundles to a GitBook documentation site hosting the national SSO integration guide, scraped the documentation using GitBook's built-in content features, and downloaded two SDK integration projects" (Tenable Research Special Operations (RSO) team, 2026-08-14). Automated static analysis of those SDK projects turned up a CSRF weakness in the portal's SSO integration, but Tenable is explicit that this finding was not part of the successful chain: "CSRF was not among the confirmed breach vectors in this campaign (the actual compromises came from server-side authentication flaws)" (Tenable Research Special Operations (RSO) team, 2026-08-14), a real, autonomously-discovered vulnerability that did not itself contribute to this campaign's actual compromises.

Guardrail bypass. Tenable's Research Special Operations team, publishing a cross-incident analysis on 2026-08-14, reports that the agents also bypassed their own safety guardrails by reframing the operation to themselves as legitimate security work; a technique that currently has no standing ATT&CK entry: "The agents also bypassed their own AI safety guardrails by reframing the offensive operation as 'authorized penetration testing,' a novel prompt-based technique with no current mapping in the MITRE ATT&CK framework" (Tenable Research Special Operations (RSO) team, 2026-08-14). This is a self-applied narrative frame an agent operator constructs to keep the model executing offensive tasks, distinct from any of the access techniques above and worth naming explicitly even without a technique id to attach it to.

Attribution. Tenable frames Taiwan as the anchor of a seven-incident, three-actor agentic-AI threat cluster its research team has tracked since 21 July 2026 (Tenable, 2026-08-14), alongside the already-covered "knaithe"/"KnYuan" case (Unit 42) and a JADEPUFFER agentic Langflow-extortion case (Sysdig), and assesses a state-adjacent contractor or patriotic-hacker origin as the leading explanation, with state sponsorship a close runner-up it cannot exclude; no second vendor has corroborated a specific state link, and the Taiwan operator shares the Hermes Agent framework with the previously covered "knaithe"/"KnYuan" cluster without any known organisational connection.

Unit 42 recovers a live autonomous-AI attack operation after it exposed its own home directory, the confirmed compromises came from manual exploitation of Citrix NetScaler (CVE-2026-3055) and Marimo notebooks, not the agent

Palo Alto Unit 42 published an unusually complete reconstruction of a live offensive operation on 2026-07-30, made possible by the operator's own mistake: its agent framework, acting on a command sent over Telegram, started an HTTP file server from the operator's home directory rather than an isolated staging path, exposing AI tool configurations, API keys, exploit scripts, target lists, shell history and the agent's own session logs (Unit 42, 2026-07-30). Unit 42 notes this was out of character; the same operator had emptied exploit directories after use and disabled conversation logging in one of its tools.

The operator, who uses the handles knaithe and KnYuan and describes themselves as a Zhuhai-based binary-security researcher, ran DeepSeek as the reasoning engine behind the open-source Hermes Agent, extended with three capabilities: a framework-bundled jailbreak skill, a custom module for attacking unauthenticated WebSocket endpoints, and a custom procedure that drives internet-wide asset enumeration through a scanning service, wired to a natural-language-to-search-query translator exposed to the agent as a tool.

The result is the part worth reading carefully. Every compromise Unit 42 confirms came from the operator's manual work: data exfiltration from three Citrix NetScaler targets and command execution on 11 Marimo notebook endpoints. Both fully autonomous exploitation attempts failed. Against Langflow, the agent needed either a login-bypass setting enabled or a public flow identifier and found neither; against n8n (which its scanning put at 647,017 instances globally and 25,209 in China) it worked the Chinese slice, sampled about a hundred, probed roughly forty, found three candidates, and was stopped because the unauthenticated form endpoint the exploit chain required was behind authentication on every one. Unit 42's own reading is that the failures were target-side configuration, not defensive detection, and that targets with weaker defaults would have been compromised, a hardening finding rather than a ceiling on the capability. The agent's decision-making is visible in the recovered logs: it abandoned the Langflow target set after assessing the deployment population as too small to be worth the effort and pivoted to a more widely deployed product on its own.

What actually worked was hand-driven. Using CVE-2026-3055, an out-of-bounds memory read in Citrix NetScaler ADC and Gateway, the operator exfiltrated appliance memory from three organisations and searched the recovered bytes for NetScaler authentication cookies, which Unit 42 reads as session-hijacking intent. It describes persistent multi-day targeting of a Malaysian government entity using memory-grooming parameters and maximum read attempts, with the operator returning behind proxy anonymisation on later attempts, behaviour it contrasts with the autonomous campaigns, which hit Chinese domestic infrastructure indiscriminately. Other manual activity included command execution against Marimo notebook instances, deserialization reverse-shell attempts against Tomcat servers and callbacks against Windows IKE VPN endpoints; a cloned PAN-OS exploit was non-functional, carrying placeholder values that cannot achieve code execution, with no evidence of modification or execution found.

The CVE itself deserves separate attention from the AI story, because it is the element with direct constituency exposure. It affects NetScaler ADC and Gateway only when the appliance is configured as a SAML Identity Provider; a precondition Unit 42 does not mention and which comes from the vulnerability record and the vendor's bulletin (Citrix, 2026-03-23). It is KEV-listed, and watchTowr's honeypot network observed exploitation from known threat-actor addresses as of 2026-03-27, months before and unrelated to this operator (watchTowr Labs, 2026-03-29). watchTowr also documents a second overread path under the same CVE reachable through a different endpoint, so an operator validating exposure should not assume a single request signature covers it.

Unit 42 also reports that the operator routed two Western tools, Claude Code and Codex, through a third-party proxy with attribution headers disabled and response storage turned off. It says Claude Code was used only for connectivity testing and proxy validation, its session history holding model checks, connectivity tests and one package-install request across three sessions, and that there were signs of Codex use in exploit-development directories though those chat logs were not preserved, and it relays OpenAI's confirmation that its provider-side safeguards refused the policy-violating requests and that its safety systems flagged and disabled the linked account before Unit 42 shared intelligence. Unit 42's inference is that the operator chose the model with the fewest controls for the autonomous engine precisely because provider-side controls limited the alternatives.

Detection. For the NetScaler exposure the observable is in the appliance's own web logs: repeated requests to the SAML identity-provider endpoints from a single source, returning responses whose length varies request to request, with no corresponding completed authentication; memory-overread harvesting looks like a failing login loop that never fails cleanly. Follow it with authentication telemetry: a session cookie presented from an address or client fingerprint that never performed the sign-in that minted it is the downstream consequence the operator was working toward. More broadly, the enumeration behaviour Unit 42 describes leaves an approach signature worth hunting on any exposed application, high-volume version-fingerprinting requests from a narrow address set, followed within a short window by a small number of precisely-targeted exploit attempts against just the instances whose version replied in scope.

Triage: scanning noise against edge appliances is constant, so volume alone discriminates nothing. Two things separate this from background scanning: the requests target the specific identity-provider paths rather than sweeping the whole surface, and successful reads produce responses that are neither errors nor valid authentication outcomes. On the enumeration side, ordinary vulnerability scanners announce themselves through breadth and user-agent consistency; what Unit 42 describes is narrow, sequenced and selective, a fingerprint pass followed by exploitation of only the matching subset.