Is Your Vulnerability Scanning Software Missing the Mark?
Understanding the risks and resolutions for mapping malicious behavior in developer code using Behavioral Computation
Old systems, new threats
The evolution and impact of software code over the last thirty years is at the core of what makes the digital landscape a marvel to behold. Unfortunately, hackers are well aware of that as well, so they have changed their methods to become more sophisticated and more difficult to detect. Legacy applications branded with a collective stamp of trust have become the preferred target of hackers. Typically, organizations use vulnerability scanning software to determine the truth of this legitimacy, but what happens when a hacker uses an unknown method outside of the expansive library of known threats? Entire infrastructures crumble, economic growth becomes economic despair, reputations face irreversible damage, and hackers reap the rewards. Vulnerability scanning software and application testing should function as a preventative step, but for this to be effective, those tools must recognize unknown malicious code in legacy applications.
The current state: vulnerability scanning
Right now, critical organizations like municipal governments and healthcare facilities use vulnerability scanning as a defense against malicious software attacks. Vulnerability Scanning involves running software from a database to detect points of entry and compares known flaws, configurations, anomalies, missing patches, and often matches threats found in malware libraries.
The approaches used for vulnerability scanning are external, internal, and environmental. While this, in theory, sounds like an all-encompassing system for finding and preventing threats, it is weak by current security standards. Since vulnerability scans only search for known threats in a database library, unknown threats can slip through the cracks. Once exposed, this vulnerability allows these unknown threats to expose anyone connected to the infected supply chain. Attacks like these have enormous effects, and because the malicious code has an unknown source, the solutions are costly and damaging to the victims.
To be effective, IT infrastructures must run vulnerability scans frequently, but the process can be risky. Human error, especially if the information is passing through many hands, creates a struggle of avoiding false positives. The entire process can be extremely time-consuming and costly without providing the robust security necessary to fend off modern hackers.
Companies that learned the hard way
Many organizations have no choice but to use legacy applications and third-party software. Often, this is the best use of their resources so that they can reach their goals. The assumption of trust and security between vendors and developers is where the gap grows with vulnerability scans. If the software appears legitimate, and the vulnerability scan shows “no known threats”, unknown threats can cause chaos in the long-term without being detected. Some companies have come face to face with this in recent years, including:
The NotPetya ransomware attack in 2017 that cost billions of dollars on a global scale
The CCleaner v5.33 supply chain malware attack in 2017 that was downloaded over four million times
The Asus 2019 hacker attack that infected over a half-million machines
The recent Orion and Solarwinds software attack in 2020 where software infected with malware was downloaded by about 18,000 organizations
Each attack involved an organization using trusted software across their supply chain, and each of these organizations is still dealing with the fallout as well. While their IT teams took the necessary steps of using vulnerability scanning, all of them failed in their security efforts because they were only hunting for known threats as opposed to unknown behaviors.
Future state: behavioral computation
Rising concern in international governments and industries brought a collective focus to cyber security standards. The call to action for higher application software security standards is finally at the forefront of most organizational developer life cycles. With the evolution of technology and the digital landscape, comes an enhanced method for malware detection: behavioral computation. This method identifies malicious behavior within software and rapidly detects cyber-threats by computing the behavior of software mathematically, providing greater visibility into malware and advanced cyber threats. Vulnerability scanning software that uses behavioral computation can hunt both known and unknown threats by reviewing the behavior of current and legacy software code to identify potential threats before they cause lasting damage.
Until now, developers have relied on a carefully maintained library of known threats to defend themselves. The moment a hacker’s attack becomes newsworthy, hacker’s change their tactics, and their behaviors. Remember, at their core, hackers are developers too. Understanding the behavior of their code is the best way to be proactive in cybersecurity by capturing the key bot/automation signatures hidden among the existing feature values or numbers.
This innovative approach to security assessments, especially if implemented at an early stage, enables solutions that were previously unavailable.
The future of DevSecOps
The age of cyber security advancement is upon us, bringing new technology to stop the bad guys in their tracks. The emergence of behavioral computation provides industries and governments with a new way to find hidden backdoors in legitimate legacy application code.
Behavioral computation introduces true intelligence to developer security operations (AKA DevSecOps), and analyzes code to discover malware behavior in executables and evasion techniques that signatures and sandboxes cannot find. The approach is scalable, proactive, and uses an adaptive approach to mitigate risks in a shorter amount of time than traditional methods.
The importance of this speaks volumes. When vulnerability scanning software can understand legacy code behavior, it can pinpoint potential threats based on mathematical algorithms and code logic. Using an automated algorithmic process, Behavioral Computation provides a fast, cost-effective solution to lessen the risk of human error and malicious attacks.
Software security becomes more effective as it becomes more rapid and accurate. As organizations migrate from vulnerability scanning to a more thorough analysis using behavioral computation, they will lessen the risk presented to consumers and operations throughout their supply chain.