State of Application Security | Datadog
State of Application Security

STATE OFAPPLICATION SECURITY

/ / / / / / / / / / /

Web application security is an important concern for organizations. Attacks have historically happened at the infrastructure and network level, but today, they increasingly target the business logic exposed by services that handle the most critical and sensitive data.

The attack surface of applications is ever increasing, with more than 25,000 vulnerabilities identified in 2022 alone. As engineering teams use a complex set of open source libraries, identifying and remediating vulnerabilities in those libraries is a perennial challenge. All of these factors make prioritization a key security concern.

The emergence of widespread attacks—for instance, targeting the Log4Shell vulnerability—has highlighted the importance of being able to rapidly discover application vulnerabilities and bridge the gap between engineering and security teams. Organizations must also focus on detecting attacks and protecting their applications as they roll out remediation efforts.

To help you improve your application security posture, we looked at real data from thousands of organizations that use Datadog Application Security Management (ASM) and Application Performance Monitoring (APM). We used runtime context to analyze which vulnerabilities really matter, which threats actually present a risk to your organization (but also which threats don't), and what characteristics of your applications and APIs have an impact on their risk.


Fact 1

Only 3 percent of critical vulnerabilities are worth prioritizing

In order to address an ever-increasing number of security vulnerabilities in their applications, organizations need to prioritize the issues that matter most. The Common Vulnerability Scoring System (CVSS) aims to help teams assess vulnerabilities based on severity (e.g., with a score of 9–10 representing “critical” severity, or the highest risk), but it paints an incomplete picture. Context matters. Knowing where a vulnerability is (e.g., in production vs. a sandbox environment)—and whether the hosting service has been actively targeted by any attacks—makes a big difference in determining next steps for remediation.

Leveraging runtime context allows us to adjust CVSS scores to be more reflective of each vulnerability’s actual severity. The score should be lowered for vulnerabilities that meet at least one of two runtime context criteria: they are detected in a non-production environment or in a service that hasn’t been attacked in the last 30 days.

When we applied these criteria to calculate adjusted scores for vulnerabilities that had CVSS scores of 9+, we found that 97 percent of them should be downgraded in severity, meaning they can be deprioritized.

Impact of Runtime Context on Vulnerability Severity

“It's extremely impactful to have very clear insights from Datadog returned—such as immediately having insight into which services are impacted, the time since detection, and how to fix them. It makes it much easier to investigate and remediate issues across all vulnerable services.”

Henri Cour
SRE, Continental Digital Services France
Fact 2

Risk grows with the number of third-party dependencies

Engineering teams utilize a large number of third-party libraries to build dynamic applications and accelerate time to market. However, from a security perspective, this presents a challenge because each library may contain vulnerabilities that put the entire application at risk.

In Java, Node.js, and Python services, we saw a marked trend—as the number of dependencies increased, so did the service’s risk (calculated based on the maximum CVSS score of its dependencies’ vulnerabilities). We also noted the same trend when we calculated adjusted vulnerability scores based on runtime context (see Fact 1). In Java, for example, all services with more than 300 dependencies had at least one vulnerability with a high or critical CVSS severity rating. Although services with more than 300 dependencies only represent 8 percent of our dataset across these three languages, we can see that they have the highest risk.

The exception to this trend was .NET, which didn’t show a clear correlation. This is possibly due to the fact that .NET vulnerabilities only accounted for 3 percent of vulnerabilities currently listed in the GitHub Advisory Database (across the four languages included in our analysis). There are a variety of possible factors that could be at play here, such as the important amount of functionality provided by the .NET standard library compared to other languages, or .NET could be getting less attention from a security research standpoint.

Share of Services with Critical Vulnerabilities by Dependency Count
NOTE: This graph does not include data from .NET services.
Fact 3

Java services have the highest risk

When comparing the different language ecosystems, one of the factors to consider is the relative severity of the known vulnerabilities in each one. To compare the four major languages used by Datadog customers, we reviewed the libraries used across a large number of organizations and services, and determined the median service risk score for each language. Java had the highest median service risk score, followed by .NET, Node.js, and Python.

One possible explanation for seeing higher CVSS scores in Java and .NET services is that those languages make it easier to access low-level primitives, potentially enabling an attacker to gain command execution. For instance, the use of reflection is common in both languages, and Java often utilizes Object-Graph Navigation Language (OGNL), which has opened the door to high-impact security issues like Log4Shell. As seen in Fact 2, .NET services have such a small number of reported vulnerabilities that their distribution might not be statistically meaningful.

At the same time, it’s important to note that CVSS scores are just one dimension to consider when evaluating the severity of vulnerabilities. In the case of Log4Shell, for example, organizations have been successful at reducing risk. We found that only 7 percent of organizations were still using vulnerable versions of the Log4j library in their Java applications—and over the course of a year, the Log4Shell vulnerability was successfully triggered in less than 0.1 percent of ASM organizations. When we analyzed data over a two-week period, we saw that only 6 percent of attacks targeted this vulnerability.

Service Risk by Language
Fact 4

Organizations still face vulnerabilities discovered in the ’90s

Despite first being discussed and exploited over 20 years ago, SQL injection and server-side request forgery (SSRF) are still being seen in modern web applications.

Over the last year, 5 percent of organizations had at least one exploitable SQL injection vulnerability, which could lead an attacker to gain unauthorized access to data or potentially even compromise the underlying host.

Our research found that while SSRF vulnerabilities are not as prevalent as SQL injections, approximately 2 percent of organizations have seen an exploitable issue in their applications over the last year.

SSRF vulnerabilities have particularly serious consequences in cloud-hosted applications because they can allow attackers to access metadata services that contain sensitive information. In OWASP’s latest Top 10, SSRF was the only specific vulnerability given its own issue.

Percent of Organizations Where the Vulnerability was Triggered
Fact 5

Three-quarters of attacks are mistargeted

Any organization that runs services connected to the internet is well aware that there is a lot of noise from automated scanners and attacks that are unlikely to succeed. In addition to attackers seeking to compromise systems, a large number of security companies regularly scan the internet for possible misconfigurations and vulnerabilities.

Looking at data over a two-week period, we saw that 74 percent of attacks would not succeed, based on runtime context. These attacks targeted endpoints that were not present in the services (66 percent of cases), tried to exploit vulnerabilities related to databases not used by those systems (31 percent of cases), or targeted languages that were not used in the application (3 percent of cases). For example, a mistargeted attack might attempt to exploit SQL injection in a system without a SQL database or try to exploit a PHP vulnerability in a Java application. This shows how important attack qualification is—security teams need to be able to sift through the noise and focus on the important attacks that require their attention.

Breakdown of Attacks
Fact 6

PHP is the top target of language-specific attacks

PHP has long been the most popular server-side language on the internet. According to W3Techs data that measured the number of sites using specific technologies, over 77 percent of websites are powered by PHP. Along with its enduring popularity, however, PHP also has a long history of security vulnerabilities and attacks.

When we looked at the attack data over a two-week period to see which languages were specifically being targeted (for example, by automated scanners), we saw that PHP, Java, and JavaScript were the main targets, and PHP was overwhelmingly the favorite. Sixty-eight percent of attacks that targeted language-specific vulnerabilities attempted to exploit PHP applications, showing that popularity does lead to increased attention from attackers.

When PHP was released in 1995, security practices were not a top concern. Since then, more modern PHP frameworks have emerged, but attackers continue to actively look for systems that still run older stacks.

Breakdown of Language-Specific Attacks
Fact 7

At least 11 percent of attacks target non-production environments

Production environments are the core of organizations’ operations. However, a successful attack on a non-production environment could also have serious consequences. Our data shows that 11 percent of attacks target non-production environments. This should be interpreted as a lower bound because we only classified environments that had obvious tags (e.g., staging, development) as non-production environments.

These attacks open up the possibility of supply chain attacks where artifacts are compromised prior to being deployed. Additionally, there may be cases where secrets are shared or sensitive data is used for testing purposes, emphasizing the need for companies to pay attention to all of their systems across every stage of the application lifecycle.

Attacks per Environment

Continuously monitor for vulnerabilities and threats in real time with Datadog Application Security Management

Request a personalized demo with a Datadog engineer

Methodology

Findings are based on data collected in March 2023.

Population

For this report, we examined data from thousands of organizations. We analyzed attack data from organizations that use Datadog Application Security Management (ASM) and vulnerability data from organizations that use Application Performance Monitoring (APM). While the results presented are biased by the fact that the data comes from our customer base, these organizations are widely diverse in terms of geography, industry, size, and maturity in their cloud journey. Consequently, we believe they provide an accurate representation of the application security issues that organizations are facing today.

Dataset

This research was performed against an anonymized dataset that does not contain personally identifiable information or sensitive information.

Vulnerabilities

Vulnerability information is a snapshot of the current state of the organizations reporting dependency information for Java, .NET, Node.js, and Python services as of mid-March 2023.

Service risk

A service's risk is defined as the maximum CVSS score of all of its vulnerabilities.

Exploitable vulnerabilities

For this report, we tracked the number of vulnerabilities that were triggered (i.e., where the attack vector reached its target without being sanitized or blocked) from March 2022 to February 2023. We define these vulnerabilities as exploitable rather than exploited because we cannot determine the intent behind the triggers. A portion of them could come from non-malicious sources, such as security scanning tools, penetration testing, or an accidental entry from a user (e.g., in the case of SQL injections). However, we expect that a significant portion of these exploits come from attackers attempting to compromise applications.

SQL injections

Datadog ASM discovers SQL injection exploitation by comparing user inputs with the SQL queries actually performed by the application. A SQL injection is known to happen if a user input injects SQL statements in a query that reaches the database. Because some vulnerabilities may be dormant or unknown, we expect that the percentage of exploitable vulnerabilities is higher than reported.

SSRF

Datadog ASM discovers SSRF exploitations by comparing user input with the HTTP requests performed by the application. An injection is known to happen if an HTTP request was directed to a host or a URL controlled by the user. Because some vulnerabilities may be dormant or unknown, we expect that the percentage of exploitable vulnerabilities is higher than reported.

Log4Shell

Datadog ASM discovers successful Log4Shell exploits by finding out when a URL injected via Object-Graph Navigation Language (OGNL) is later retrieved by the application as a Java class file. Because some vulnerabilities may be dormant or unknown, we expect that the percentage of exploitable vulnerabilities is higher than reported.

Mistargeted attacks

We determined which threats could really impact a system by leveraging runtime context and distributed tracing data from Datadog ASM customers over a two-week period.

Non-production environments

We analyzed Datadog ASM data over a two-week period and classified development, staging, and test environments based on each organization's environment tags for their applications. While this will not capture every application's setup, it provides a means of classifying the data based on each organization's understanding of their applications. Our finding represents a lower bound because we only counted environments that had obvious tags (e.g., staging, development) as non-production environments in our analysis.