Addressing Primary Open-Source Security Challenges

In the modern era of computing and data storage, the most critical element of any system is the software on which it runs. While hardware is still important, devices have developed to the point where the differences between compromised and secure networks, databases, and files come down to code, not physical security measures. 

One thing that has not changed since the earliest days of computing, however, is the rapid rate at which technology develops. Likewise, the importance of keeping up is a major factor for any business that hopes to stay relevant or secure. Due to this, as well as the high cost of proprietary software tools, open-source software (OSS) has come to dominate the world of coding. 

What Is Open-Source Software?

In the world of software development, the term “open-source” refers to any software with accessible source code that anyone can modify and share freely. Protocols, algorithms, and even fully-developed programs and games can be created with open-source coding. 

In most cases, open-source code is adapted and integrated into programs where it can be useful. Because source code is the part of the software that users don’t see or interact with, common open-source code is, at times, worked on by hundreds or even thousands of independent parties that can be used seamlessly without any outwardly-recognizable signs. 

In the early days of computing, there were very few dedicated professional programmers, and so the early internet was almost entirely made up of open-source code. The efforts of enthusiasts and professionals alike were aided by the network effect as the internet grew in popularity, allowing more people to contribute and refine the very protocols that were connecting them. 

Today,  many companies employ in-house software engineers; however, much of the code that we still use relies on the efforts of open-source developers. In fact, a 2019 report by Gartner found that 96% of codebases contain at least some open-source code. 

Advantages of Open-Source Software

There are many reasons why open-source coding is still so common. When compared to private development, open-source programs have many advantages. By giving programmers direct access to a program’s source code, the software can be continuously improved and expanded. This allows developers to add new features and fix bugs as they arise, rather than having to rely on the software’s original developer to address these concerns. 

The ability to grow and adapt quickly is essential to success in today’s increasingly fast-paced work environment. Organizations attempting to stay on top (or simply keep up with the market) have needs that evolve rapidly. Because of this, many companies look for solutions with the least amount of friction between development and implementation. 

Dangers of Open-Source Software

For all of the advantages that open-source software brings, there are a number of very significant risks stemming from the very aspects that make it so adaptable. And as prevalent as open-source coding is, a staggering number of organizations lack the structure to address these risks. A 2022 report by the Linux Foundation found that less than half of businesses had an open-source security policy in place for OSS development or usage. 

This lack of preparation can open the door to a wide variety of cyberattacks. Because anyone can access the source code of these programs, any flaws or vulnerabilities could quickly become public knowledge. Malicious actors can also freely examine the code that underlies any programs utilizing a piece of open-source software. 

The exploitation of these vulnerabilities can have wide-ranging negative impacts on all sorts of businesses. Everything from proprietary business data to private medical records can be compromised by attacks utilizing loopholes in open-source code. 

On a more sophisticated level, there are numerous ways in which open-source code can be compromised by hackers, causing anyone who then uses it to fall into their hands. For instance, if a code is compromised before it is used, any flaws built into it will remain there unless specifically eliminated. This may sound simple, but the reality is far more challenging. Unless security experts know precisely what to look for and where to look for it, detecting malicious lines of code can be virtually impossible. Even attempting to do so requires knowledge of whether the code has been compromised to begin with. In most cases, however, vulnerabilities do not become known until they have already been exploited. 

Types of Open-Source Security Risks

To better understand how the aforementioned attacks can occur, let’s examine some of the most common methods that hackers use to inject malicious code into open-source programs. 

Upstream Server Attacks 

In upstream server attacks, malicious entities infect a system “upstream” as it is uploaded onto a computer system or device. To accomplish this, malicious code is added to the software at its source, often through a malicious update, infecting all users “downstream” as they download it. 

Midstream Attacks 

Midstream attacks are fundamentally similar to upstream attacks, but instead of tampering with code at its initial source, they target intermediary elements. These include software development tools and updates that pass on the malicious code from there. 

CI/CD Infrastructure Attacks 

Another variation of the upstream attack model, CI/CD infrastructure attacks introduce malware into the development automation infrastructure of an open-source code requiring “continuous integration” or “continuous delivery” steps. 

Dependency Confusion Attacks 

Unlike the previous three types of attacks, Dependency Confusion Attacks exploit private, internally-created software dependencies by registering a new dependency with the same name in a public repository with a higher version number. The malicious code is then optimally placed to be pulled into software builds in place of the latest legitimate version of the software. 

Case Study: Log4Shell

Regardless of whether hackers compromise open-source code by one of the above methods or learn of a genuine loophole from an open hacking forum, once a door has been opened, any and all data within the compromised system is immediately vulnerable. Some measures can be taken to avoid some of these, but even the biggest companies have fallen prey. 

One of the most dangerous and well-publicized instances of open-source software falling vulnerable to attack came in 2021 when a code-execution vulnerability exploit for Log4j was released. At the time, Log4j was a virtually ubiquitous open-source utility used in countless popular applications, including Microsoft, Amazon, and Twitter servers. 

Referred to as “Log4Shell,” the vulnerability was first reported in November of that year after being identified in the popular game Minecraft. The code exploit was also published in a tweet a few weeks later, leading to numerous forums warning users that hackers could execute malicious code on servers or clients running the Java version of Minecraft. 

Millions of servers were left vulnerable by the exploit. The Apache Software Foundation assigned Log4Shell the highest-possible severity rating in the Common Vulnerability Scoring System (CVSS), and the director of the US Cybersecurity and Infrastructure Security Agency (CISA) called the exploit a “critical” threat. Using Log4Shell, attackers were able to install blockchain crypto, steal system credentials, and access sensitive data before a patch was released. 

Truly Secure Data with Sertainty 

The simultaneously derivative and interconnected nature of the modern internet makes avoiding open-source code a practical impossibility. For this and other reasons, traditional perimeter security falls notably short when it comes to keeping malicious actors out of your system. 

Because of this omnipresent threat, Sertainty leverages proprietary processes through its UXP Technology that enable data to govern, track, and defend itself – whether in flight, in a developer’s sandbox, or in storage. These UXP Technology protocols mean that even if systems are compromised or accessed from the inside, all data stored in them remains secure. 

At Sertainty, we know that data is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered what it means for data to be intelligent and actionable, helping companies move forward with a proven and future-proof approach to cybersecurity needs. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. Open-source security breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be. 

AI Optimization and Anonymization

Today, artificial intelligence is no longer the far-off dream it once was. Tools like Midjourney, ChatGPT, and others have taken off in the last year, bringing with them a barrage of questions.  Many cybersecurity experts, and those entrusted with handling sensitive information, have pegged data privacy as the likeliest potential threat that these programs pose to organizations. 

The capabilities of AI are surmounting daily. Cybersecurity risks are mounting in step. From the first moment an AI Engine is optimized, it starts processing datasets. Partly because of this, effective data anonymization has become critical due to various compliance regimes and consumer protection laws. Companies hoping to utilize the power of artificial intelligence must factor in which datasets, audiences, and business problems it seeks to ascertain their predictions. 

What Is AI Optimization? 

Before testing an AI program, it must be optimized for its intended application. While, by definition, these programs are always learning, the initial training and optimization stage – which is defined by Volume, Variety, and Variance, is an essential step in the AI development process. 

There are two modes of AI training: supervised and unsupervised. The main difference is that the former uses labeled data to help predict outcomes, while the latter does not. 

The amount of data available to AI dictates whether developers can extract inputs to generate a significant and nuanced prediction in a controlled environment. Depending on data accuracy, developers will intervene and recast an existing outcome into a general output and reiterate the unsupervised processing w for better quality control and outcome. 

Supervised Learning

In this context, labeled data refers to data points that have been given pre-assigned values or parameters by a human. These human-created points are then used as references by the algorithm to refine and validate its conclusions. Datasets are designed to train or “supervise” algorithms to classify data or predict outcomes accurately. 

Unsupervised Learning

While no machine learning can accurately occur without any human oversight, unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention, making them “unsupervised.” 

While more independent than supervised learning, unsupervised learning still requires some human intervention. This comes in the form of validating output variables and interpreting factors that the machine would not be able to recognize. 

Data Anonymization in Machine Learning

The majority of machine learning advances of the past three decades have been made by continuously refining programs and algorithms by providing them with huge volumes of data to train on. ChatGPT, one of the most popular AI platforms today, is an open-source chatbot that learns by trolling through massive amounts of information from the internet. 

For all of their impressive capabilities, however, AI programs like ChatGPT collect data indiscriminately. While this means that the programs can learn very quickly and provide comprehensively detailed information, they do not fundamentally regard personal or private information as off-limits. For example, family connections, vital information, location, and other personal data points are all perceived by AIs as potential sources of valuable information. 

These concerns are not exclusive to ChatGPT or any other specific program. The ingestion of large volumes of data by AI engines magnifies the need to protect sensitive data. 

Likewise, in supervised machine learning environments, anonymization for any labeled data points containing personal identifiable information (PII) is key. Aside from general concerns, many AI platforms are bound by privacy laws such as HIPAA for health-related data, CCPA legislation in California, or the GDPR for any data in the EU. 

Failing to protect the anonymity of data impacted by these laws can result in steep legal and financial penalties, making it crucial that anonymization is properly implemented in the realm of AI and Machine Learning. 

Pseudonymization vs. Anonymization

When discussing data privacy, the word anonymization is almost always used, but in reality, there are two ways of separating validated data points from any associated PII. In many cases, rather than completely anonymizing all data files individually, PII is replaced with non-identifiable tags (in essence, pseudonyms). 

Perhaps the most famous large-scale example of this is blockchain technology. While personal data such as real names or other PII are not used, in order for the record-keeping chain to function, all data for each user must be linked under the same pseudonym. While some people consider this to be sufficiently anonymous for their purposes, it’s not as secure as true anonymization. If a pseudonym is compromised for any reason, all associated data is essentially free for the taking. 

True anonymization, on the other hand, disassociates all identifying information from files, meaning that the individual points cannot be linked to each other, let alone to a particular person or parent file. 

Because of this, many security experts prefer to avoid the half-measure of pseudonymization whenever possible. Even if pseudonymous users are not exposed by error or doxxing, pseudonymized data is still vulnerable in ways that fully anonymized data is not. 

Already, some AIs are becoming so sophisticated that they may be able to deduce identities from the patterns within pseudonymized datasets, suggesting that this practice is not a secure replacement for thorough anonymization. The more data algorithms are trained on, the better they get at detecting patterns and identifying digital “fingerprints.” 

Other AI-Driven Anonymization Scenarios

In the current landscape of ever-more-capable machine learning, the value of proper data anonymization is greater than ever. Aside from the vulnerabilities within AI-driven frameworks, external threats driven by digital intelligence present new challenges, as well. 

For one thing, artificial intelligence is able to exploit technical loopholes more effectively than human hackers. But beyond that, AI is also increasing threats targeted at social engineering. Recently, users found that ChatGPT was able to generate phishing emails that were notably more convincing than many human-generated attempts. This will undoubtedly lead to increasingly sophisticated attempts to access private data. As such, new tactics must be employed to properly secure and anonymize data before it becomes exposed to artificial intelligence.

Anonymized Smart Data with Sertainty

Sertainty’s core UXP Technology enables Data as a Self-Protecting Endpoint that ensures the wishes of its owner are enforced. Sertainty’s core UXP Technology will also enable developers working within AI environments such as ChatGPT to maintain ethical and legal privacy with self-protecting data. Rather than attempting to hide PII and other sensitive data behind firewalls, Sertainty Self-Protecting Data files are empowered to recognize and thwart attacks, even from the inside. 

As a leader in self-protecting data, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself in today’s digital world. These protocols mean that if systems are externally compromised or even accessed from the inside, all data stored in them remains secure. 

At Sertainty, we know that the ability to maintain secure files is the most valuable asset to your organization’s continued success. Our industry-leading Data Privacy Platform has pioneered what it means for data to be intelligent and actionable, helping companies move forward with a proven and sustainable approach to their cybersecurity needs. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. With the proliferation of human and AI threats, security breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be.

Is Blockchain Really as Secure as it Seems?

For nearly a decade and a half, cryptocurrency and the blockchain technology that powers it have played an increasingly central role in cybersecurity and online privacy discussions. Bitcoin and other cryptocurrencies have been touted as truly anonymous ways of storing and spending money, and popular perception remains, which is that blockchain itself is “unhackable.” 

While the idea of digital currency or decentralized data is not a new one, functioning blockchains are still relatively new. The technology became viable in 2008 when a person (or group of people) using the name ‘Satoshi Nakamoto’ introduced the first digital currency that addressed decentralization’s past issues by creating the first viable blockchain. Since then, various applications for blockchain technology have been developed, mostly due to its inherently incorruptible nature. 

How Does the Blockchain Work? 

Sometimes referred to as distributed ledger technology, a blockchain is a type of online database that maintains records in the form of “blocks” of information that are cataloged in chronological order. This creates a “chain” of data blocks, each representing an event in the history of the complete system. Each time a new transaction is completed, a new block is added, continuing the ledger of information. 

Blockchains come in two primary forms, public and private. In public chains, users from anywhere can join, becoming a part of the chain of nodes, sending and receiving transfers of data and currency that are then included in the chain. On the other hand,  private chains only allow users that have been granted permission to access transaction data. Both private and public chains can also be “permissionless” or “permission restricted,” depending on whether or not users within the network have the ability to validate transactions or merely utilize the existing nodes. 

It’s worth noting that blockchain technology can be used to send, receive, and track where files are sent. However, the actual data within the blocks remain private. The data itself is only accessible to the user(s) with the correct digital ‘keys.’ The databases where information shared using a blockchain is stored still have the same features and vulnerabilities, regardless of how securely that data may be shared.

A Reputation for Inherent Security

As we mentioned earlier, a common perception among those who use any form of blockchain technology is that this type of system is impenetrable. Like conventional digital ledgers, the record of events is intended to be permanent, with each block becoming unchangeable once it’s accepted into the chain. However, unlike traditional systems, blockchain data is stored across multiple nodes hosted in different locations. The wider the web of nodes spreads, the more fail-safes the system has. 

The result is a theoretically corruption-proof system. In theory, if a secure node (or nodes) were to be compromised, the rest of the blockchain would recognize the discrepancies and prevent false information from being accepted. 

Blockchain’s Limitations

While all of this makes large blockchains fundamentally more reliable than single-source records, no system is completely immune to threats. The dangers to the blockchain can come from users within a network or outside of it. These dangers must be considered before you put all of your faith into a system on reputation alone. 

51% and Sybil-Type Attacks

While the record of shared information is protected by the wide variety of verification data centers in the system, malicious actors can target the network itself. The two most obvious threats to blockchain networks come in for form of “51%” attacks and “Sybil-Type” attacks. 

During 51% of attacks, hackers attempt to generate enough data verification nodes to outnumber the number of legitimate nodes. If a single party can gain control of more than half of a blockchain’s nodes (hence the name), the information they present will be seen by the system as the ‘real’ record, and the previously existing, legitimate chain will be overruled.

Additionally, 51% of these attacks are only practical in smaller networks. Major blockchains, like Bitcoin, are far too vast for any one group to take control. Additionally, these attacks can be mitigated using a permission-restricted system so only verified users can create new nodes. 

Sybil-type attacks, so-called after a book of the same title, refer to an attack by users who attempt to create an overwhelming number of false transactions with false identities. These attacks flood the chain with unreliable information and overwhelm the system. Sybil-type attacks share some similarities with other blockchain threats, but they are easier to create in public chains. These attacks can be prevented if there is a high cost to create new accounts to discourage users from creating enough to disrupt the chain. 

Compromised User Accounts and Routing Attacks

Like with many digital systems, the greatest vulnerabilities of all come from the human component. While correctly moderated blockchains may be extremely resistant to intervention, users in the system are always vulnerable to phishing, RAT attacks, and other social engineering scams that jeopardize credentials and digital keys. 

Due to the impact of human error, data shared via the blockchain can be verified as coming from a legitimate source; however, there’s no guarantee of safety once it has reached its destination. Crypto wallets, private databases, and more can all still be breached by inside or outside actors.

Cryptocurrency Exchange Trustworthiness

If sending money over blockchain, users need to familiarize themselves with the crypto exchange. Although many tout the safety and security of the blockchain, using cryptocurrency for transactions isn’t safe as what was once alluded to. With the recent collapse of FTX and loss of $2 billion in user funds, businesses and individuals alike could be at the mercy of how these private organizations are handling both data and money. 

Truly Secure Data with Sertainty 

Regardless of the enhanced legitimacy of decentralized ledger systems, data breaches remain a significant concern for any conventionally-protected network. Utilizing a public or private blockchain can be one part of your data protection strategy. However, to guarantee that network breaches don’t leave you vulnerable, you must ensure that your data files are truly secure. 

Rather than rely on a series of firewalls and trust that those with access are legitimately allowed to be there, Zero Trust security gives data the ability to protect itself. Following this methodology, Sertainty has redefined how information is protected to ensure data privacy even where firewalls fail. Using cutting-edge protocols and embedding intelligence directly into datasets, Sertainty leverages proprietary processes that enable data to govern, track, and defend itself. These protocols mean that even if systems are compromised, data remains secure. 

As the digital landscape evolves and networks become more widely accessible, Sertainty is committed to providing self-protecting data solutions that evolve and grow to defend sensitive data. Instead of focusing on your network’s inherent shortcomings, we enable our partners to safely and confidently embrace the potential of a new online-oriented world. Data breaches may be inevitable, but with Sertainty, privacy loss doesn’t have to be.

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