The Risks of Using LLMs for Threat Detection

By /

Listen to this Post

Featured Image
Large Language Models (LLMs) are powerful tools for assisting in cybersecurity investigations, but they should not be relied upon for creating threat detections due to high risks of false negatives and false positives. Instead, they excel in supporting human analysts by providing context, risk-based alerting, and dynamic response playbook generation.

You Should Know:

Why LLMs Fail in Threat Detection

  1. False Positives/Negatives: LLMs may misinterpret benign activity as malicious (false positives) or miss actual threats (false negatives).
  2. Lack of Real-Time Context: They rely on training data, not live threat intelligence.
  3. Overconfidence in Outputs: LLMs generate plausible-sounding but incorrect detections.

Better Alternatives: Detection Engineering

Instead of LLM-generated detections, use:

  • Sigma Rules (for log-based detection): 
    title: Suspicious Process Execution 
description: Detects unusual process execution patterns 
logsource: 
category: process_creation 
detection: 
selection: 
CommandLine: </li>
<li>" -nop -w hidden -c " 
condition: selection

  • YARA Rules (for malware detection): 

    rule Detect_Phishing_Doc { 
meta: 
description = "Detects malicious Office docs with macros" 
strings: 
$s1 = "AutoOpen" 
$s2 = "powershell.exe -nop -w hidden -e" 
condition: 
all of them 
}

  • SIEM Queries (Splunk example for ransomware detection): 

    index=winlogs EventCode=4656 ObjectType=File AccessMask=0x10000 
| stats count by AccountName, ObjectName 
| where count > 10

Enhancing Investigations with LLMs

While LLMs shouldn’t create detections, they can assist in:
– Log Analysis: Summarizing large logs.
– Playbook Automation: Generating step-by-step response guides.
– Threat Context: Explaining attack techniques (e.g., “What is Living Off The Land (LOTL)?”).

Linux/Windows Commands for Manual Threat Hunting

  • Linux: 
    Check for unusual processes 
ps aux --sort=-%cpu | head -n 10

Detect unexpected network connections 
sudo netstat -tulnp | grep ESTABLISHED

Find hidden files (common in ransomware) 
find / -name "." -type f -exec ls -la {} \;

  • Windows (PowerShell): 

    Check for suspicious scheduled tasks 
Get-ScheduledTask | Where-Object { $_.State -eq "Running" }

Detect unusual service installations 
Get-WmiObject -Class Win32_Service | Where-Object { $<em>.StartMode -eq "Auto" -and $</em>.State -eq "Running" }

Hunt for encoded PowerShell commands 
Get-WinEvent -LogName "Windows PowerShell" | Where-Object { $_.Message -like "-EncodedCommand" }

What Undercode Say

LLMs are not yet reliable for autonomous threat detection but serve as excellent co-pilots for security teams. Detection engineering—using structured rules like Sigma, YARA, and SIEM queries—remains the gold standard. Always validate LLM-generated insights with manual investigation and established detection frameworks.

Expected Output:

  • Accurate detections via rule-based systems.
  • LLM-assisted investigations for context and automation.
  • Reduced false positives/negatives through human-in-the-loop validation.

Prediction

As LLMs evolve, they may improve in detection accuracy, but human oversight will remain critical for the foreseeable future. Hybrid approaches (AI + human analysts) will dominate enterprise security.

URLs:

IT/Security Reporter URL:

Reported By: Activity 7338869326147399681 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

Join Our Cyber World:

💬 Whatsapp | 💬 Telegram

Related Posts: