Mindgard Wins Enterprise Security Tech 2024 Cybersecurity Top Innovation Award
Mindgard is proud to announce its recognition as a winner of the Enterprise Security Tech 2024 Cybersecurity Top Innovations Award.
Fergal Glynn
Cyber attacks are on the rise. For today’s organizations, it’s no longer a matter of “if” there will be a cyber threat against their systems, but rather “when.” Traditional castle-and-moat approaches—which focus on protecting a network by building a strong perimeter defense—are no longer relevant in this threat ecosystem, which is why more organizations are investing in red teaming.
Red teaming is a proactive approach to cyber security that simulates real-world attacks to uncover vulnerabilities before hackers can. By thinking like an attacker, red teams test organizational resilience and help refine defense strategies against real-world threats.
In this guide, we’ll break down common techniques used by red teams that put your defenses to the ultimate test.
Red teaming techniques mimic the strategies used by real attackers to breach data and gain unauthorized access. There are a variety of red teaming tools that can help teams carry out these tests. While every red team is different, many use these techniques to test organizations’ defenses.
Social engineering isn’t a new type of attack, but it’s persisted because it’s so effective. Phishing emails are the most common type of social engineering attack, where the red team sends deceptive emails to trick employees into sharing credentials or clicking malicious links.
However, there are other types of social engineering red team techniques. For example, some red teams leave infected USBs around the office or parking lot to lure employees into using them. Others use false pretexts and fake scenarios to manipulate employees into providing sensitive information.
Physical security measures are just as important as digital security, although many organizations tend to overlook this critical component of cyber security. Ethical hackers try to bypass physical security measures with this red teaming technique. This is achieved by cloning badges, tailgating an authorized person into restricted areas, finding blind spots in your security cameras, or even picking locks.
It may sound like something out of a spy film, but it’s a commonly used tactic by real-world hackers that organizations must test for.
APTs are targeted and difficult to detect, which is why many red teams use this method to measure an organization’s defenses against high-level attacks. There are various APTs, including:
It’s difficult for some organizations to accept, but employees are often the cause of data breaches. Whether accidental or intentional, insider threats can compromise accounts and lead to data theft.
This red teaming technique involves accessing employee accounts to cause as much damage as possible. Ideally, the team targets employees with elevated privileges to measure how much damage they can do.
An organization’s internal security may be top-notch, but what about its vendors? Attackers are increasingly targeting third-party vendors and suppliers to gain access through vendors with weak security.
Some supply chain attacks inject malicious code through firmware backdoors or distribute malware through fake software updates. Others use social engineering attempts, like phishing, to access sensitive information or credentials.
The growing use of AI in business operations, cybersecurity, and customer interactions has introduced new vulnerabilities that require specialized red teaming techniques.
Traditional red teaming focuses on exploiting vulnerabilities in IT infrastructure, networks, and human behavior. Unlike traditional IT systems, AI models don’t follow rigid rule-based programming—they learn, adapt, and generate predictions based on patterns. This dynamic nature makes AI platforms especially vulnerable to unique types of attacks that traditional red teaming techniques might overlook.
Let’s explore some of the key techniques used in red teaming for AI platforms and why they diverge from conventional methods.
Adversarial attacks are a cornerstone of AI red teaming. These attacks involve crafting inputs designed to deceive machine learning models into making incorrect predictions or classifications.
Traditional red teaming doesn’t deal with manipulating algorithmic outputs. Adversarial attacks are unique to AI systems and require red teams to have a deep understanding of model architectures, training data, and decision boundaries.
Data poisoning involves tampering with the training data used to build AI models. By injecting malicious or biased data into the training set, red teams can compromise the model’s performance or introduce vulnerabilities that attackers can later exploit.
Traditional red teaming focuses on exploiting live systems. Data poisoning, on the other hand, targets the development phase of AI systems. This technique is particularly relevant for AI systems that rely on continuous learning or retraining.
Model inversion attacks aim to reverse-engineer AI models to extract sensitive information about the training data. For instance, in facial recognition systems, attackers may attempt to reconstruct images of individuals from the model’s outputs. Extraction attacks involve stealing the entire model or its parameters to replicate it elsewhere.
Traditional systems prioritize access control over intellectual property protection. AI red teaming, however, must assess how easily external actors can reconstruct the model’s functionality.
AI systems are often vulnerable to biases present in their training data or design. AI red teamers actively probe models for discriminatory behavior or unfair outcomes, such as racial or gender bias in decision-making algorithms. This involves testing the model with diverse datasets and edge cases to uncover hidden biases.
Traditional red teaming doesn’t address the ethical or societal implications of AI systems. AI red teaming, however, must consider the broader impact of biased or unfair AI decisions.
With the rise of generative AI models like ChatGPT, red teams now focus on techniques like prompt injection and jailbreaking. Prompt injection involves crafting inputs that manipulate the model into generating harmful, biased, or unintended outputs. Jailbreaking refers to bypassing the model’s built-in safeguards to elicit restricted or dangerous responses.
These techniques are specific to generative AI and natural language processing (NLP) systems. To use these techniques effectively, red teams must understand the nuances of language models and their guardrails.
Red teaming techniques are designed to mimic real-world attacks. These simulations provide valuable insights into an organization’s security posture, helping it to address weaknesses before attackers can exploit them.
Companies must continuously test, adapt, and strengthen their defenses to stay ahead of attackers. Investing in red teaming isn’t just about finding weaknesses—it fosters a culture of security that protects data, employees, and customers from ever-growing cyber risks. The proliferation of AI, for instance, has introduced new and unique risks and requires specialized red teaming techniques designed to evaluate an AI system’s vulnerabilities.
Red teaming doesn’t have to use internal resources, either. Organizations rely on Mindgard to test their defenses with smart, automated red teaming. Request a demo now to see how Mindgard protects AI models from evolving threats.
Yes. Given how popular cloud-based solutions like Google Cloud, AWS, and Azure are, organizations should conduct red team testing in the cloud. Red teaming can test cloud misconfigurations, API vulnerabilities, privilege escalation risks, and identity & access management weaknesses.
Cloud-based attacks sometimes also involve attempts to exfiltrate data from cloud storage, compromise virtual machines, or bypass security controls like MFA and IAM.
While red teaming is much more effective than alternatives like penetration testing, it isn’t perfect. Organizations often encounter issues like:
Every red team uses various techniques to test an organization’s defenses, but these are the most common types of simulations:
Red teaming for AI platforms involves testing vulnerabilities specific to machine learning models, data pipelines, and model behavior—risks that don’t exist in traditional IT systems. Unlike traditional approaches that focus on network infrastructure, applications, and endpoints, AI red teaming simulates adversarial machine learning attacks, prompt injections, model theft, and data privacy breaches.
These techniques evaluate how models respond to manipulated inputs, biased data, or attempts to infer sensitive training information. As AI systems become more integrated into critical operations, specialized AI red teaming helps ensure model reliability, fairness, and security against evolving threats.
