How to Mitigate Buffer Overflow Attacks in the Age of Big Data and AI


Buffer overflow featured in the news recently after several security issues were discovered in the Chrome browser. Google issued a fix for a zero-day flaw in Chrome along with other problems, particularly a heap buffer overflow in SQLite.

What is a buffer overflow, and why is it important to be familiar with it in light of the looming presence of big data and the rise of artificial intelligence? Read along to learn more about this software vulnerability that highlights the importance of sensible data management in applications.

Buffer overflow: A common vulnerability

The threats of a buffer overflow attack are quite common. They are not new; they have been in existence for a few decades now. Buffer overflow vulnerabilities have been spotted in software, especially web browsers and mobile apps. Notably, these security weaknesses are associated with several security breaches, including the “Code Red” worm that wreaked havoc on computers at the turn of the 21st Century.

The buffer overflow vulnerability exists when a program tries to write data to a buffer that is more than what the buffer can handle. The buffer serves as temporary storage for data that is being used or transmitted to different components of a program or between two or more programs, devices, or networks. The writing of more data than what a buffer is meant to handle results in the overwriting of adjacent data, which can cause unwanted consequences, including the malfunctioning of a program. Buffer overflows can also cause unexpected or unintended operations like the execution of malicious code, which can enable security breaches and the takeover of a device.

Because of its potential to enable malicious arbitrary code execution, buffer overflow is regarded as one of the most dangerous software security weaknesses. This vulnerability was found in the Microsoft Exchange Server’s code, and it made it possible for bad actors to execute arbitrary code on the server. It is at the heart of the Heartbleed bug, which allowed threat actors to access sensitive data from web server memories. The Apache Struts vulnerability reported in 2017 is also a form of buffer overflow weakness that allowed cybercriminals to obtain sensitive data from the Equifax credit reporting agency. On the other hand, the WannaCry ransomware succeeded in infecting systems because it took advantage of a buffer overflow vulnerability in the Windows Server Message Block protocol.

How big data impacts the buffer overflow problem

Big data provides various advantages, but it can also worsen the buffer overflow problem. For one, it facilitates the expansion of attack surfaces. The storage and processing of huge amounts of data from a multitude of sources make it more difficult to oversee and secure attack surfaces. It results in more software complexity, which aggravates the difficulty of mitigating buffer overflow vulnerabilities.

The use of distributed architectures in big data systems also creates more opportunities for buffer overflow problems to emerge. In particular, the use of various nodes and components makes memory management more challenging.

Moreover, there’s the issue of untrusted data. Some big data systems do not employ or may have difficulties implementing data validation and sanitation procedures. This may not be the biggest attack vector involving buffer overflow vulnerabilities, but it is a potential security weakness nonetheless. This problem is worsened by real-time processing, wherein compromises are made to ensure rapid processing at the expense of security measures such as input validation and boundary checks.

AI’s impact on buffer overflow

Artificial intelligence similarly has significant consequences on the buffer overflow vulnerability. It is a driver of greater complexity, as AI systems often involve various components and algorithms that make it more challenging to detect and mitigate overflow problems. Many apps nowadays integrate AI, which means the increased complexity is not just limited to a few classes of applications.

On the other hand, the advent of AI-powered code builders potentially compounds the overflow problem. As organizations embrace AI to rapidly churn out apps, it is inevitable to become too reliant on AI’s capabilities that security takes the backseat or becomes an afterthought. AI may also utilize open-source or free libraries and frameworks that are riddled with vulnerabilities, resulting in more software with security issues.

Moreover, AI can serve as a tool for adversarial attacks. Cybercriminals can develop machine learning systems that automatically detect buffer overflow vulnerabilities to narrow down potential targets. They can test systems for susceptibility to memory overflow attacks at a pace faster than conventional reconnaissance. Additionally, AI can automate the exploitation of detected vulnerabilities.

How to effectively address buffer overflow threats

Buffer overflow attacks are a serious threat, but they are not exactly overwhelming. With security best practices, it is possible to prevent them or ensure that the vulnerabilities do not exist, to begin with.

  • Prevent the emergence of the vulnerability. The buffer overflow vulnerability is preventable. Developers can avoid this security weakness by implementing data validation checks to make sure that apps only process valid data. It can also be stopped by using memory protection mechanisms to ascertain secure memory allocation and access with the help of technologies like Address Space Layout Randomization (ASLR). Additionally, it is important to stick to security best practices like updating software regularly and conducting regular security audits.
  • Harness big data and AI. The drivers of buffer overflow threats may also serve as tools to address the problems. Big data is used by security frameworks and threat intelligence platforms to keep up with the latest vulnerabilities and attacks. Similarly, AI can be used to automate the detection of security issues and the prompt response to them. In other words, you can use cybersecurity frameworks and advanced AI-powered cybersecurity platforms to address buffer overflows and various other security threats.
  • Avoid being too dependent on AI. Artificial intelligence has advanced significantly over the years, but it is still inexpedient to fully depend on it to produce software or systems. Avoid creating and deploying apps that are solely or largely generated by AI. Observe due diligence in using generative AI tools, but make good use of AI to detect vulnerabilities and maximize security posture. Many cybersecurity platforms integrate artificial intelligence to bolster threat detection, mitigation, prevention, and remediation.

Recognizing the threats and harnessing the advantages

Big data and AI are not inherently good or bad. They can be used to create threats but they can also serve as tools to quell attacks. The buffer overflow vulnerability demonstrates the importance of understanding the beneficial and adversarial sides of new technologies. The threats may be rising, but the available solutions are also improving.

The post How to Mitigate Buffer Overflow Attacks in the Age of Big Data and AI appeared first on Datafloq.

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