DeepSeek + Hermes Agent: The New Era of 'Set and Forget' Autonomous Attacks?
Just caught the Unit 42 report regarding a Chinese-speaking threat actor (tracked as knaithe / KnYuan) utilizing DeepSeek via the open-source Hermes Agent framework. What stands out isn't just the use of AI, but the level of autonomy achieved.
The workflow reported is essentially:
- Initiation: Single command via Telegram.
- Recon: Agent identifies internet-facing systems.
- Selection: autonomously selects public exploits.
- Execution: Launches attacks with zero further operator input.
While the specific CVEs weren't listed in the snippet, the reliance on public exploits implies N-day vulnerabilities are the primary vector. The implication for SOC teams is the compression of the OODA loop—attackers can now recon and exploit in near-real-time without human latency.
For detection, we should be looking for agents that mimic human browsing but execute at superhuman speeds. We might need to tune our WAFs to look for specific Hermes fingerprints or the specific User-Agent strings associated with these Python-based frameworks.
Here is a basic Python snippet to detect high-velocity scanning often characteristic of autonomous agents:
def detect_autonomous_agent(log_entries):
"""Detects potential autonomous agent activity based on request velocity."""
requests_per_ip = {}
for entry in log_entries:
ip = entry['ip']
requests_per_ip[ip] = requests_per_ip.get(ip, 0) + 1
# Flag IPs hitting multiple distinct endpoints rapidly
suspects = {ip: count for ip, count in requests_per_ip.items() if count > 50}
return suspects
Is anyone else seeing traffic patterns that suggest automated decision-making rather than standard scripts? How are we defending against agents that adapt their exploit selection on the fly?
This is a significant evolution from standard botnets. The 'adaptability' factor is what worries me. Traditional scanners hit every port; these agents likely only hit ports where they know an exploit exists, making them stealthier.
From a blue team perspective, I'm focusing on heuristic anomalies rather than just signature matching. If a server is checking for a specific obscure N-day vulnerability 5 minutes after it's disclosed, it's likely an agent.
We've seen a similar uptick in 'smart' scanning. The key is that these agents still rely on logic flaws or unpatched vulnerabilities. Aggressive patch management is the only real cure, but I know that's not always feasible.
One tip: Block generic User-Agents if your business allows it. Many of these Python frameworks fail to spoof the UA correctly or default to 'python-requests'. A simple mod_security rule helps:
apache SecRule REQUEST_HEADERS:User-Agent "@contains python-requests" "id:1001,phase:1,deny,status:403,msg:'Python Request UA Blocked'"
While patching is crucial, these autonomous agents often pivot to credential stuffing if exploits fail. From an IAM perspective, we need to assume the initial perimeter might be breached. I recommend monitoring for high-frequency authentication anomalies that indicate automated tooling.
Here's a quick KQL query to hunt for anomalous sign-in bursts:
SigninLogs
| where ResultType == "0"
| summarize count() by AppDisplayName, UserPrincipalName, bin(TimeGenerated, 5m)
| where count_ > 10
This helps catch agents trying to brute-force or spray credentials after their initial recon.
Valid point on the autonomy. If the agent decides on exploits dynamically, deception tech becomes a strong countermeasure. High-interaction honeypots can waste the agent's resources and provide IOCs based on its specific selection logic. For those wanting to test this, deploying a simple pot using Docker is quick:
docker run -p 2222:22 -p 80:80 cowrie/cowrie
Monitoring how the agent reacts when it hits this can reveal exactly what criteria the LLM uses for target selection.
From an API security perspective, the real risk is these agents automating logic abuse rather than just scanning for unpatched vulns. We need to look for 'behavioral perfection' in traffic. A human reconnaissance varies in timing and sequencing; an agent often hits endpoints with mathematical rigidity.
You can hunt for this consistency in your logs. Here's a simple Python heuristic to detect suspiciously rigid request intervals:
import numpy as np
def detect_rigidity(timestamps):
intervals = np.diff(timestamps)
return np.std(intervals) < 0.1 # Threshold for 'too perfect'
Since these actors often aim for cryptojacking, watching for resource hijacking is vital. Autonomous agents try to blend in, but they rarely mask the payload's CPU signature perfectly. I recommend setting up alerts for sustained high CPU usage from non-critical services.
For Linux environments, you can hunt for hidden miners checking for processes masquerading as system updates:
ps aux | awk '{print $11}' | sort | uniq -c | sort -rn | head
If you see generic names like `kworker` or `systemd` consuming resources disproportionately, you might be dealing with an autonomous agent's payload.
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