Autonomous Post-Exploitation: Hermes AI Agent Hits Thai Ministry of Finance
Just caught the report regarding the Hermes AI agent targeting the Thai Ministry of Finance. The attacker reportedly disabled the 'human-in-the-loop' safety protocols, essentially turning the AI into a unattended autonomous bot for post-exploitation.
The agent didn't just sit there; it actively hunted for root privs and crawled the file system for treasury data. This blurs the line between automated scanning and intelligent, adaptive lateral movement. Standard baselines might fail here because the AI likely generates unique commands on the fly rather than dropping known payloads.
For those hunting this, I'd suggest focusing on the velocity of command execution rather than just the strings. An AI agent doesn't type like a human; it operates at machine speed.
Here is a Python snippet to detect high-frequency process creation events that might indicate an automated agent at work:
import psutil
import time
threshold = 10 # processes per second
pid_history = []
while True:
current_pids = [p.pid for p in psutil.process_iter(['pid'])]
new_pids = set(current_pids) - set(pid_history)
if len(new_pids) > threshold:
print(f"Alert: High velocity process execution detected: {new_pids}")
# Trigger alert logic
pid_history = current_pids
time.sleep(1)
Is anyone else seeing AI-driven tools in their honeypots yet? How do we distinguish between aggressive DevOps automation and a rogue AI agent?
The velocity check is a solid start, but in high-frequency trading environments, this would trigger constant false positives. We've had better luck correlating process execution with session anomolies. If an automated sequence kicks off from a terminal session that didn't originate from a known bastion host or VPN, we quarantine immediately.
We tested Hermes in a lab last month. It's terrifying because it doesn't rely on standard exploits. It uses the LLM to read 'man' pages and construct novel privilege escalation chains based on the specific kernel version it finds. It effectively rewrites its attack script for every host. Signature detection is useless here; we need pure behavior analysis.
This confirms that disabling the safety rails on these agents is as dangerous as exposing an RDP port to the internet. We're pushing a policy update that blocks all outbound traffic from non-approved AI API endpoints. If the agent can't phone home for logic updates, it's blinded.
Valid points, everyone. If the agent reads documentation to write exploits, it needs a writable environment. We should consider enforcing immutability in production clusters. By making the root filesystem read-only, you effectively neuter an AI's ability to save new scripts or tools it compiles on the fly.
kubectl patch deployment -p '{"spec":{"template":{"spec":{"containers":[{"name":"","securityContext":{"readOnlyRootFilesystem":true}}]}}}}'
Combined with eBPF monitoring, this stops the 'write-execute' loop Hermes relies on.
To support RedTeam_Carlos’s point on reading documentation, we need visibility into file access patterns. Since Hermes relies on reading man pages, rapid consecutive reads from /usr/share/man should trigger an alert. From a compliance perspective, ensuring these interactions are logged is critical for forensics.
I recommend adding an auditd rule to monitor access to system documentation:
-w /usr/share/man -p r -a exit,always -F key=doc_access
This ensures a verifiable audit trail if an agent attempts to learn the environment's configuration.
Agreed with the immutability angle. Since the agent dynamically generates commands, static signatures will likely fail. We should focus on detecting the translation of documentation into action, specifically flagging when documentation readers spawn unexpected child processes.
# Sigma rule concept
detection:
selection:
ParentImage|endswith: '/man'
ChildImage|endswith: '/bash'
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