Thinking Smarter Not Harder About AI-Based Threats
Risks, Challenges, and Smarter Countermeasures
Introduction
Happy New Year, ABCbyD community! As we kick off another year filled with resolutions, reinventions, and (if you're here in the Northeast) relentless winter activities, I took a moment to reflect on some interesting cybersecurity developments from late 2024. Over the break, I indulged in my fair share of holiday cookies and caught up on a few flagged articles. One theme stood out: AI-based threats are evolving rapidly, becoming more creative, effective, and scalable than ever.
In this blog, we’ll explore the rise of AI-powered threats, how adversaries leverage AI to bypass traditional defenses, and why behavioral detection methods remain our strongest line of defense. We’ll walk through examples like WormGPT, PoisonGPT, and advanced AI-generated malware, and share how smarter approaches—many of which we already have—can help turn the tide.
The Threat Landscape: AI’s Dark Side
AI is a double-edged sword. While it powers breakthroughs across industries, it’s also reshaping cyberattacks. Here are two examples from 2024 that underscore its malicious potential:
AI-Generated Malware:
In December, researchers found that AI-generated malware could evade detection 88% of the time. By tweaking malicious code in subtle yet effective ways, AI eliminates the need for manual methods like hex-editing, making signature-based defenses feel obsolete.WormGPT-Enabled Phishing:
Generative AI tools like WormGPT automate phishing emails and Business Email Compromise (BEC) attacks, crafting convincing messages at scale. Once easy to spot due to poor grammar or odd phrasing, phishing emails now require heightened scrutiny and skepticism to detect.
These examples highlight how AI isn’t just a threat multiplier—it’s re-architecting the way cyberattacks are designed and executed.
Challenges in Countering AI-Based Threats
How can this happen? Aren’t LLMs safeguarded against generating malicious code? Sort of…the challenge is erecting boundaries and setting guardrails for LLMs limits functionality. Two problems make this boundary-setting even more challenging:
The Context Window Problem:
Modern language models (LLMs) rely on context windows to process information and generate outputs. While longer windows enable greater functionality, they also introduce vulnerabilities. For instance, the ‘Bad Likert Judge’ jailbreak exploited these weaknesses to increase attack success rates by over 60%.The Rate of Change:
AI evolves rapidly, as seen with OpenAI’s o3 release just after launching o1. Each new model brings capabilities that can empower innovation—or exploitation. Keeping up with this pace is daunting but essential.
These challenges can feel overwhelming, but they also underscore the importance of continuously refining our defenses and thinking smarter—not harder—about countermeasures.
New AI-Based Threats
A few noteworthy new AI-based threats caught my attention, from polymorphic malware to weaponized GPTs. Here are some that stick out to me:
AI-Generated Malware: Polymorphic malware, created with AI, generates subtle variations that bypass traditional signature-based solutions. These tools leverage natural language processing to tweak payloads in ways that appear benign but remain malicious.
WormGPT and Phishing Automation: WormGPT has improved phishing campaigns by enabling attackers to automate highly personalized and convincing email attacks at scale. These AI-powered tools remove technical barriers, making advanced attacks accessible to less-skilled cybercriminals.
Weaponized Misinformation with PoisonGPT: PoisonGPT, a tampered language model, was designed by researchers at Mithril Security to spread misinformation while performing normally in other contexts. This highlights the risks of compromised models uploaded to public repositories, underscoring the need for stronger model integrity checks.
Smarter Countermeasures: Threat Hunting in SIEM and EDR
The good news? If we use them effectively, we already have tools to address many of these challenges. Threat hunting in existing SIEM and EDR environments is about spotting behaviors that don’t fit the norm—patterns that even AI-based threats can’t fully disguise. Think of it this way: while AI can generate clever attacks, it still needs to interact with systems, files, or memory in ways that can leave clues.
SIEMs might flag a process trying to read sensitive data repeatedly, or your EDR might notice unusually complex (or “high entropy”) files, which can hint at AI-generated malware. By focusing on these behaviors rather than chasing specific tools or signatures, you’re essentially teaching your defenses to look for what attackers are doing, not just how they’re doing it.
I’ve included a few simple examples here, and I hope these can jog memories or spur action. Here’s how we can think smarter:
note to the reader: these are examples of hunts, if you have specific techniques that have worked for your or would like more information, please let me know in the comments
Behavioral Detection Remains King
Signature-based methods struggle against AI’s adaptability, but behavioral detection provides a robust alternative. Instead of hunting for specific tools like Mimikatz, focus on detecting credential theft behaviors, such as unusual memory access or file reads.
Example SQL Query (SIEM):
index=endpoint
| search process_name='lsass.exe'
| stats count by user, file_path
| where count > 5
This query flags processes attempting to access sensitive files, a common indicator of credential theft.
Endpoint-based Threat Hunting
Endpoint telemetry can reveal patterns that AI-based threats leave behind. For example, monitoring for high entropy in files can help detect AI-generated malware or obfuscated payloads.
Example Python Script (Entropy Detection):
def calculate_entropy(file_path):
with open(file_path, 'rb') as f:
data = f.read()
frequency = [data.count(byte) / len(data) for byte in range(256)]
return -sum(p * math.log2(p) for p in frequency if p > 0)
if calculate_entropy('/path/to/file') > 7.5:
print('High entropy detected!')
If you’re not stoked about SQL queries or pumped about Python, no worries. The reality is, we have the tools to tackle aspects of the AI-based threat vectors (as it stands today). While threats are evolving, behavior doesn’t change. We’ll be digging into how to hunt specifically for AI-based threats in a future blog post.
Final Thoughts
AI is changing cybersecurity, flooding our inboxes, streamlining our defenses and augmenting the attacker’s playbook. The risks are real, but so are the opportunities to fight back. By leveraging smarter detection strategies, integrating behavioral insights, and fostering collaboration, we can stay ahead in this ever-evolving battle.
The smarter AI threats become, the smarter our defenses must be. Let’s embrace the challenge, rethink our strategies, and turn AI into a force for resilience rather than risk.
Happy New Year ABCbyD community, stay secure and stay curious!
Damien
🔥🔥🔥🔥!