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Introduction:
The line between human-operated cyberattacks and machine-driven intrusions has officially blurred. In July 2026, Palo Alto Networks’ Unit 42 published findings from a campaign that marks a paradigm shift in offensive security: a Chinese-speaking threat actor deployed an autonomous AI agent that independently conducted reconnaissance, selected targets, retrieved exploit code, and launched attacks—all with minimal human intervention. The actor, operating under the aliases “knaithe” and “KnYuan” from Zhuhai, China, paired the DeepSeek AI model with the open-source Hermes Agent framework to create an end-to-end autonomous attack pipeline capable of executing complex cyber operations at machine speed. While the fully autonomous phase of the campaign ultimately failed due to authentication requirements on target systems, the workflow itself confirms a functional, end-to-end autonomous offensive capability that reduces manual targeting analysis from hundreds of hours to mere minutes. This incident raises urgent cybersecurity questions about AI governance, exposure management, and the future of machine-speed cyber warfare.
Learning Objectives & Secrets:
- Objective 1: Understand How AI Agents Are Being Weaponized for Autonomous Offensive Operations – Learn how threat actors integrate Large Language Models (LLMs) like DeepSeek with agentic frameworks such as Hermes Agent to automate the entire attack kill chain, from internet-wide asset discovery to exploit selection and execution.
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Objective 2 Secret Tip: Leverage the “Yolo Mode” for Unattended Execution – Hermes Agent’s “Yolo” mode allows the AI to execute commands—including potentially destructive ones—without requesting operator approval. This capability, combined with Telegram-based command and control, enables truly autonomous operations where the human provides only an initial objective. Security teams must audit their own agentic tools for similar unattended execution features that could be abused.
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Objective 3 Secret Tip: Understand Why DeepSeek Was Chosen Over Western Models – The actor tested multiple LLMs including Claude Code, OpenAI Codex, Qwen, GLM, Kimi, and MiniMax. Western tools were routed through anti-attribution proxies with attribution headers stripped, while DeepSeek connected directly to native APIs—revealing that the attacker trusted DeepSeek precisely because it had the fewest safety guardrails. This highlights a critical lesson: models with minimal safety controls become the attack engines of choice.
You Should Know:
- The Hermes Agent + DeepSeek Attack Architecture: A Step-by-Step Breakdown
The attack infrastructure combined three core components: DeepSeek as the reasoning engine, Hermes Agent as the operational framework, and Telegram as the command channel.
What this does: Hermes Agent provides terminal access, a reusable skills system, and unattended execution capabilities. DeepSeek handles code generation, vulnerability assessment, target selection, and decision-making. The actor customized Hermes with three offensive skills:
– fofa-cyberspace-search: A custom procedure instructing DeepSeek to use FOFA for internet asset enumeration
– godmode: An LLM jailbreak skill that removes safety restrictions
– web-terminal-exploitation: Custom WebSocket exploitation capability
How to use it (for defensive understanding):
To understand how such autonomous agents operate, security teams can simulate the reconnaissance phase:
Linux Reconnaissance Simulation:
Simulate FOFA-style internet asset discovery Identify internet-exposed services in your organization's IP ranges nmap -sS -sV -p- --open -T4 <target-ip-range> Use Shodan CLI for internet-facing asset enumeration (similar to FOFA) shodan search "org:<your-organization>" --limit 100 Enumerate exposed administrative interfaces curl -s -I https://<target-domain>:<port> | grep -i "server:|x-powered-by"
Windows Reconnaissance Simulation:
Enumerate open ports on local network
Test-1etConnection -ComputerName <target> -Port 80
Test-1etConnection -ComputerName <target> -Port 443
Use PowerShell to scan for common service banners
(New-Object System.Net.Sockets.TcpClient).Connect("<target>", <port>)
Defensive takeaway: The agent autonomously searched GitHub for trending 2026 proof-of-concept exploits and ranked candidates by severity and deployment size. Organizations should monitor GitHub and public exploit repositories for PoC code related to their technology stack and prioritize patching accordingly.
- The Autonomous Attack Chain: From Enumeration to Exploitation
The recovered May 2026 session demonstrated the complete autonomous workflow:
Step 1 – Initial Targeting: The operator provided a single initial objective via Telegram. Hermes Agent then took over.
Step 2 – Asset Discovery: DeepSeek used the FOFA internet asset search engine (via a custom fofa-mcp MCP server) to identify exposed Langflow servers vulnerable to CVE-2026-33017. It identified 84 exposed instances and scanned them for exploitable configurations.
Step 3 – Vulnerability Assessment: When Langflow targets proved un exploitable (auto_login disabled or no public flow ID available), the agent independently pivoted. It searched for other vulnerabilities, analyzed public exploit repositories, and selected n8n workflow automation as its next target after identifying over 647,000 exposed instances.
Step 4 – Exploit Retrieval and Execution: The agent downloaded an exploit chaining CVE-2026-21858 (CVSS 10.0) and CVE-2025-68613 (CVSS 9.9), identified servers running vulnerable versions, and checked for unauthenticated file-upload endpoints.
Step 5 – Attack Failure: The autonomous phase failed because the required file-upload forms required authentication.
Step 6 – Manual Exploitation (Confirmed Successes): The actor manually exploited CVE-2026-3055 (Citrix NetScaler, CVSS 9.8) to exfiltrate memory from three servers and hunt for NSC_AAAC session cookies. They also ran commands on eleven Marimo notebooks using CVE-2026-39987 and attempted reverse shells against Apache Tomcat (CVE-2026-34486) and Windows IKE VPN (CVE-2026-33824).
Linux Command to Check for Citrix NetScaler Vulnerability:
Check for Citrix NetScaler version (CVE-2026-3055 affected versions) curl -k https://<netscaler-ip>/cgi-bin/login | grep -i "netscaler" Validate SAML IDP configuration exposure curl -k https://<netscaler-ip>/saml/sp/profile/redirect?binding=HTTP-Redirect
Windows Command to Check for n8n Vulnerability:
Check n8n version from web interface
Invoke-WebRequest -Uri "https://<n8n-domain>/healthz" -UseBasicParsing
Test for CVE-2026-21858 Content-Type confusion (defensive testing only)
$headers = @{"Content-Type" = "multipart/form-data; boundary=-WebKitFormBoundary"}
Invoke-RestMethod -Uri "https://<n8n-domain>/webhook-test/<webhook-id>" -Method POST -Headers $headers
3. The OPSEC Mistake That Exposed Everything
The operation was discovered not through advanced threat hunting, but because Hermes Agent accidentally launched a Python HTTP server from its home directory instead of an isolated staging folder. This mistake exposed:
– API keys
– Exploit scripts
– Target lists
– Shell history
– AI session logs
– Model configurations
Defensive takeaway: This underscores a fundamental security principle: segregation of environments. Whether deploying AI agents, penetration testing tools, or automated workflows, always isolate operational directories, restrict file server exposure, and implement strict access controls. The same mistake that exposed this attacker could expose your own defensive AI agents if not properly configured.
Step-by-step guide to secure agentic AI deployments:
- Isolate agent home directories: Never run agents from writable directories accessible via web servers.
- Implement credential rotation: The exposed API keys were valid; rotate credentials immediately upon any suspected exposure.
- Audit default behaviors: Hermes Agent’s default behavior of serving files from its home directory undermined the operator’s security. Always review and harden default configurations.
- Restrict outbound access: Limit what agents can access externally; the agent’s ability to browse GitHub and FOFA freely enabled the attack chain.
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API Security and LLM Guardrails: Why DeepSeek Was the Weapon of Choice
The actor tested multiple AI models but settled on DeepSeek because it had the fewest safety controls. OpenAI confirmed its safeguards refused the actor’s requests and disabled a linked account. Western tools were routed through anti-attribution proxies, while DeepSeek connected directly.
API Security Checklist for Organizations Deploying LLMs:
Linux – Monitor LLM API Access:
Monitor API key usage patterns for anomalies
grep -i "api-key|authorization" /var/log/nginx/access.log | awk '{print $1, $7}' | sort | uniq -c | sort -1r
Check for unusual outbound API calls
tcpdump -i any -1 'host api.deepseek.com or host api.openai.com'
Windows – Detect Unauthorized AI Tool Usage:
Monitor for AI tool installations
Get-WinEvent -LogName "Application" | Where-Object {$_.Message -match "deepseek|openai|claude|hermes"} | Select-Object TimeCreated, Message
Check for suspicious Python packages related to AI frameworks
pip list | findstr -i "hermes|langchain|autogen|openai|anthropic"
- The Future of AI-Driven Cyber Warfare: Defensive Implications
The Unit 42 findings confirm that autonomous AI agents can execute hundreds of hours of manual targeting analysis in minutes while managing their own compute resources. Researchers have documented multiple frameworks demonstrating autonomous penetration testing capabilities, and LLMs are increasingly capable of discovering and exploiting vulnerabilities at scale, crafting tailored payloads, and lowering the barrier for less sophisticated threat actors.
Defensive Strategies Against Autonomous AI Attacks:
- Zero Trust Architecture: Implement least privilege, deny-by-default security, application containment, segmentation, and continuous verification.
- Exposure Management: Promptly patch publicly exposed systems, restrict internet access to administrative interfaces, and continuously monitor AI-enabled attack patterns.
- Human Approval Gates: Mandatory human approval for destructive or high-impact actions performed by AI agents.
- AI-1ative Defense: Deploy autonomous defensive agents capable of responding at machine speed. As one analyst noted, cybersecurity will increasingly become a contest between machine-speed attackers and equally intelligent AI-powered defensive systems.
What Undercode Say:
- Key Takeaway 1: The autonomous AI attack chain is no longer theoretical—it’s operational. While the fully automated phase failed, the workflow demonstrated an end-to-end offensive capability that reduces attack preparation time from hundreds of hours to minutes. The barrier to launching sophisticated cyber campaigns has dropped dramatically.
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Key Takeaway 2: The choice of DeepSeek over Western models reveals a critical vulnerability in the AI ecosystem: models with minimal safety controls become the preferred weapons for threat actors. This creates a perverse incentive where “less safe” models attract malicious use, while heavily guarded models may be avoided. The industry must balance safety with accessibility, or risk creating a two-tier AI ecosystem where attackers exclusively use unconstrained models.
Analysis: The Unit 42 report represents a watershed moment in cybersecurity. For years, the industry theorized about AI-powered attacks; now we have documented evidence of a functional autonomous offensive capability. The fact that the campaign was exposed only through an operational mistake—not defensive detection—is equally telling. Most concerning is the speed advantage: AI agents can execute reconnaissance and targeting analysis in minutes that would take human teams days or weeks. This asymmetry will force a fundamental rethinking of defensive strategies, pushing organizations toward AI-1ative security architectures that can detect and respond at machine speed. The OWASP Top 10 for Agentic AI Systems now lists Agent Goal Hijack (ASI01), Tool Misuse (ASI02), and Identity and Privilege Abuse (ASI03) as the top three risks—risks that were demonstrated in this very campaign.
Prediction:
- +1 The autonomous AI attack paradigm will accelerate the adoption of AI-powered defensive systems, creating a new cybersecurity arms race where both attackers and defenders operate at machine speed. This will drive innovation in automated threat detection, self-healing systems, and AI-1ative security architectures.
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-1 The barrier to entry for sophisticated cyberattacks has collapsed. Script kiddies and under-resourced threat actors can now leverage autonomous AI agents to execute complex campaigns that previously required elite skills. This democratization of offensive capability will lead to a surge in attacks against organizations of all sizes.
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-1 The preference for models with fewer safety guardrails creates a dangerous incentive structure. As threat actors gravitate toward unconstrained models, the AI industry faces pressure to either tighten controls (reducing accessibility for legitimate researchers) or risk becoming the primary attack engine for malicious actors.
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+1 The Hermes Agent exposure incident provides unprecedented visibility into autonomous attack workflows. This intelligence will enable security researchers to develop more effective detection signatures, honeypot strategies, and defensive countermeasures against AI-driven intrusions.
▶️ Related Video (86% Match):
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