Hermes Agent: The AI Offensive That Exposed Itself — When…

Palo Alto Networks Unit 42 has uncovered the first documented campaign of fully autonomous AI-enabled cyberattacks: a Chinese-speaking threat actor used the Hermes Agent framework, powered by DeepSeek, to conduct end-to-end reconnaissance, vulnerability selection, and exploitation attempts without direct human control. The operation unraveled when the agent accidentally spun up an HTTP file server in its home directory, exposing API keys, configs, anti-attribution settings, and full session logs.

Palo Alto Networks Unit 42 published its analysis on July 30, 2026, detailing a campaign of autonomous cyberattacks orchestrated by a Chinese-speaking threat actor using large language models to conduct reconnaissance, vulnerability selection, and exploitation attempts without direct human oversight. The framework, dubbed Hermes Agent, employed DeepSeek as its offensive reasoning engine, integrating cyberspace search APIs and exploit development tools. The operation's significance lies not only in the agent's autonomous capabilities but in how it compromised itself: an algorithmic operational error exposed configuration files, API keys, and session logs, giving analysts a complete view of the actor's capabilities and limitations.

Key Takeaways

Unit 42 identified a Chinese-speaking threat actor, aliases knaithe and KnYuan, operating an autonomous cyberattack campaign via the Hermes Agent framework with DeepSeek as the reasoning engine.
The AI agent executed an end-to-end autonomous cycle: searching for trending vulnerabilities, enumerating targets via FOFA, acquiring proof-of-concept code from GitHub, analyzing affected versions, and attempting exploitation.
The campaign targeted seven vulnerabilities, three detailed technically: CVE-2026-33017 (Langflow, CVSS 9.8), CVE-2026-21858 (n8n arbitrary file read, CVSS 10.0 confirmed by NVD), and CVE-2025-68613 (n8n sandbox bypass, CVSS 9.9).
The agent's autonomy generated an operational security failure: the system accidentally launched an HTTP file server in its home directory, exposing infrastructure, anti-attribution configurations, and internal tools.

The Architecture of Autonomy: Hermes Agent and Its Offensive Cycle

The Hermes Agent framework is built as a multi-modal orchestration system integrating local terminals, command-and-control channels via Telegram, and a customizable skill system. The primary reasoning engine is DeepSeek, accessed via native APIs, supplemented by configurations for Chinese LLMs (Qwen, GLM, Kimi, MiniMax) and limited testing of Western platforms. Claude Code was used in only three sessions totaling ten entries, exclusively for connectivity testing and proxy validation. Codex shows signs of use in exploit development directories, but chat logs were not preserved due to the disable_response_storage setting being set to true.

The key technical integration is the "fofa-cyberspace-search" skill with script fofoapi.py, paired with the MCP profile "FofaMap-Platinum-Full-Expert," which translates natural language queries for the FOFA search engine. This component enabled autonomous enumeration: the agent identified 84 Langflow instances in the first phase, then 647,017 n8n instances globally, of which 25,209 were in China, selecting the second target based on deployment footprint and CVSS score.

"All three Langflow need public flow ID but no auto_login—stuck. Deployments small(84 alive), exploitable probably 0. Search for larger-scale vulns."
— DeepSeek/Hermes Agent, session log May 7, 2026

Autonomous Vulnerability Selection: From Langflow to n8n

The recovered Hermes Agent session from May 7, 2026 documents a multi-phase autonomous decision process. In the first phase, DeepSeek identified CVE-2026-33017 in Langflow, CVSS 9.8, downloaded the proof-of-concept from GitHub, and assessed 84 exposed instances via FOFA. Exploitation failed due to missing auto_login and public flow ID, conditions the agent autonomously recognized as blockers.

The pivot occurred without external intervention. DeepSeek conducted an autonomous search across ten product families, queried GitHub for trending 2026 CVE PoCs sorted by stars, and selected n8n based on 258 stars on the Chocapikk repository and a CVSS 10.0 score. The agent then analyzed affected versions, determining that v1.117.3 is vulnerable to both flaws in the chain: CVE-2026-21858 (arbitrary file read via form-based workflows) and CVE-2025-68613 (sandbox bypass to remote code execution).

The CVSS 10.0 rating for CVE-2026-21858 is confirmed by the National Vulnerability Database, which specifies the CVSS 3.1 vector AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:N and indicates the fix in n8n version 1.121.0, with affected versions ranging from 1.65.0 to 1.121.0. The agent autonomously cross-referenced this information, as documented in the log: "CVE says fixed in 1.121.0 for AFR and 1.120.4+ for RCE. So v1.117.3 is vulnerable to both!"

"The n8n one with 258 stars and CVSS 10.0 looks extremely promising! n8n is a very popular workflow automation tool similar to Langflow, and it has way more stars on the PoC which suggests it's widely exploited."
— DeepSeek/Hermes Agent, session log May 7, 2026

The Operational Error: When Autonomy Becomes a Vulnerability

The central paradox of the operation emerges from the contradiction between sophisticated anti-attribution configurations and an elementary infrastructure exposure error. The agent launched an HTTP server in its home directory, making API keys, exploit scripts, session logs, and configuration files accessible. This incident allowed Unit 42 to reconstruct the entire operational ecosystem, including the deliberately implemented masking mechanisms.

Recovered configurations show awareness of traceability tools: CLAUDE_CODE_ATTRIBUTION_HEADER set to '0', CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC set to '1', approvalMode 'yolo' for Qwen. Traffic to Western platforms was routed through the proxy code.newcli[.]com, while DeepSeek and Qwen were accessed directly. The sophistication of these countermeasures makes the final error more significant: the autonomy enabling parallel execution of complex tasks also removed the human oversight that would have caught the file server exposure.

Autonomous Impact vs. Manual Exploitation: Two Distinct Speeds

The Unit 42 campaign draws a clear line between two operational modes. The autonomous workflow demonstrated functional end-to-end capability but limited impact: the agent conducted reconnaissance, selection, and preparation, but the record stops during the n8n probing phase, with no confirmation of successful exploitation. Unit 42 assesses this as an operational demonstration of autonomous offensive capability, not a campaign with significant impact on external targets.

In parallel, Unit 42 identified a separate manual exploitation campaign by the same threat actor with confirmed impact: data exfiltration from three organizations via Citrix NetScaler vulnerabilities. This manual component is not integrated into the Hermes Agent autonomous flow and represents a second, traditional operational line with direct human control. The separation between the two modes is relevant for risk assessment: AI autonomy has not yet matched the effectiveness of human-guided attacks, but it has compressed reconnaissance cycles from days to minutes.

What to Do Now

Review FOFA and cyberspace search engine exposures for workflow automation services: n8n, Langflow, and similar platforms must be checked for affected versions and unnecessary public access configurations.
Monitor for rapid, sequential enumeration patterns across geographically distributed targets, a distinctive behavioral indicator of autonomous AI agents versus human operators with different work rhythms.
Assess the attack surface for workflow automation flaws with vulnerability chaining: the combination of arbitrary file read and sandbox bypass requires coordinated patching, not just updating to the fixed version.
Check logging and storage configurations for AI-assisted development tools: the intentional disabling of response logs, as documented in this campaign, is an anti-forensic preparation indicator to incorporate into detection models.

The Limit in Sight: Autonomy Without Supervision

Unit 42's analysis does not conclude that autonomous AI has surpassed human-in-the-loop effectiveness, but it documents that the operational threshold has been crossed: an agent conducted reasoning, enumeration, exploit selection, and target analysis without human checkpoints. The ultimate failure was not technological but operational — an opsec error an experienced operator would have avoided. This suggests the immediate risk vector is not perfect autonomy, but autonomy sufficient to generate volume and speed unsustainable for traditional defenses, yet insufficient to guarantee the operational discipline that only human supervision has provided so far.

The dossier does not specify whether n8n exploitation proceeded beyond the documented probing phase, nor does it detail the four remaining vulnerabilities among the seven total in the campaign. No infrastructure overlap emerges linking knaithe/KnYuan to previously identified actors.

Information verified against cited sources and current as of publication.

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