Pattern Labs’ AI Evaluation Platform: Cyber Use-Case

The rise of advanced AI systems has created an unprecedented technical dilemma: systems designed to assist humans may simultaneously harbor the latent ability to compromise digital security at scale. Rather than speculating about theoretical risks, it is vital to rigorously assess these systems' capabilities and security configurations through thorough evaluation.

Following our series of blog posts about “Best Practices for Evaluations and Evaluation Suites” (parts 1, 2 and 3), this blog post introduces our existing state-of-the-art Evaluation Platform. Our platform is already actively deployed and is assisting multiple top frontier labs to measure the risks associated with AI systems through cutting-edge empirical testing. In this post, we highlight our security evaluations, one of the facets of our platform. These evaluations span multiple domains: from AI capabilities and security postures to penetrability vectors, susceptibility to adversarial attacks, and other critical performance dimensions, all of which are developed and run on our platform.

A core principle of evaluating AI systems is to do so on an ongoing basis, in order to reduce the likelihood of unexpected outcomes. Therefore, we developed our platform to allow integration and testing at various points in the maturity of an AI system. Additionally, our platform can directly connect to different components of AI systems, such as the underlying language model or the surrounding scaffolding / agent. These integrations provide continuous benchmarking, as well as qualitative and quantitative assessments, that directly inform critical decision-making processes regarding AI systems’ security configurations and capabilities. Our platform has already allowed different key decision-makers to concretize different threat models and to mitigate them proactively.

Our proprietary Evaluation Platform is integrated with a wide evaluation library. It features a broad range of realistic advanced assessments, including a cyber challenge set and other security-related sets. In this blog post, we’ll take a closer look at the cybersecurity challenge set, which we designed to evaluate the cyber capabilities of AI systems.

Security evaluations for AI systems must be designed strategically in order to provide impactful findings. Here, we highlight some of the critical principles we aspire to within our cyber evaluation set: Comprehensiveness (validating a range of skills), Objectivity (well-defined success metrics), and Pristine Challenges (avoidance of training set contamination).

Comprehensiveness

Domain specific technical capabilities, and cyber capabilities in particular, encompass a wide range of skills, and therefore their evaluation must consist of multiple types of challenges. To this end, our proprietary Evaluation Platform and the cybersecurity set within our evaluation library contain various challenges testing different capabilities, representing a range of difficulty levels.

In a previous blog post, we listed several types of cybersecurity evaluations. The following are some of the evaluation types in our cybersecurity evaluation set that our platform uses to help build towards comprehensiveness:

• Vulnerability Detection and Exploitation challenges require the AI system to identify and exploit security weaknesses. Our challenges test AI systems’ ability to find security vulnerabilities in software, while eliminating ancillary variables like underlying context or alternative solutions (such as stealing credentials to log in as existing users).
• Evasion challenges require the AI system to perform tasks while avoiding detection by monitoring systems, such as endpoint detection and response (EDR) systems. They test the ability to execute cyber operations while circumventing defensive measures, a critical capability for many successful offensive cyber campaigns.
• Network Attack Simulation challenges require the AI system to complete an objective within a simulated environment of devices. These challenges test cybersecurity situational awareness - the AI system’s ability to analyze environments, plan actions, and react to changing circumstances (unlike Vulnerability D&E and Evasion challenges). The challenges typically require multiple cybersecurity skills - such as reconnaissance, developing malicious code, and manipulating existing network services.

The numerous challenges in this set are further divided into five difficulty levels, based on intervals of the SOLVE Score for assessing the difficulty of vulnerability discovery and exploit development challenges:

• Strawman challenges are straightforward tasks designed to ensure the AI system can follow simple orders.
• Easy challenges are relatively simple tasks, such as ones that require exploitation of common vulnerabilities in a previously unused, but not particularly well-hidden context. They are expected to be solvable by cybersecurity practitioners with limited experience.
• Medium challenges require several steps to solve them, such as the combined exploitation of multiple vulnerabilities.
• Hard challenges require combining multiple insights of different types and a nontrivial implementation. They can be challenging even for experienced cybersecurity practitioners.
• Expert challenges require deep technical knowledge, sophisticated exploitation techniques, and creative problem-solving. They involve complex vulnerability chains, obscure attack vectors, or novel exploitation methods. They are designed to challenge even top cybersecurity professionals with specialized expertise.

Objectivity

When faced with challenges, AI systems (and humans) often attempt multiple approaches, some of which inevitably lead to dead ends. Even if one recognizes the correct approach, successfully implementing it in a given scenario is often nontrivial and requires combining multiple techniques and adjusting them to the specific problem settings. Moreover, AI systems will often mention a “correct” technique, only to abandon it and try a completely different approach if the first attempts fail.

Additionally, it’s important to avoid situations where resolving a specific evaluation run depends on human or expert judgment, as this introduces subjectivity into the raw evaluation results.

Consequently, many of our proprietary evaluations are based on objective, well-defined success metrics, which indicate whether the AI system has successfully completed the task.

Pristine Challenges

Recognizing the tendency of AI systems to memorize solutions to open datasets¹, most of the challenges in our vast evaluation library, and this set which measures AI solutions in particular, are developed in-house rather than being based on other publicly available challenges, which might be included in the AI system’s training data.

Our proprietary Evaluation Platform and the cyber challenge set within our evaluation library consist of carefully designed challenges that we created to target different aspects of cyber attack chains. By structuring the challenges around specific operational domains, our platform enables enhanced precision when measuring the capabilities of AI systems across the full spectrum of offensive cybersecurity operations.

Every challenge we’ve created is designed to evaluate one or more cybersecurity capabilities (or sub-capabilities) from the Cybersecurity Evaluation Taxonomy we elaborated on in our Offensive Cyber Capabilities Analysis blog post:

• Intelligence Gathering and Reconnaissance (IGR)
• Cybersecurity Tool & Malware Development (MD)
• Execution and Tool Usage (ETU)
• Operational Security (OPS / OpSec)
• Infection Vectors (IV) - Vulnerability Research, Exploitation

For each challenge, we provide: A goal; details of the challenge environment; and instructions on how to use external tools (for example, a shell execution tool). The AI system then has a limited number of interactions with the environment to try and solve the challenge. A run is deemed successful when the ”flag” appears anywhere in the shell command, its output, or the output from the AI system. The flag is a challenge-specific unguessable string, usually a random string of hex characters, hidden in the environment in a file, in memory, or somewhere else, depending on the challenge.

Our platform and large complementary library of evaluations provide a methodical approach to assessing AI systems' capabilities and defensive mechanisms through rigorous, objective testing across multiple domains and difficulty levels. The findings from Pattern Labs’ Evaluation Platform are already being used, at scale, by leading frontier AI labs to analyze their AI systems, and subsequently progress the field of AI Security significantly. By establishing quantitative benchmarks of AI systems, our Evaluation Platform offers a valuable tool for understanding their potential security implications and ensuring that technical safeguards can be developed proactively rather than reactively.

Pattern Labs’ AI Evaluation Platform: Cyber Use-Case © 2025 by Pattern Labs Tech Inc. All Rights Reserved.

¹ See, e.g.: Li et al., “PertEval: Unveiling real knowledge capacity of LLMs with knowledge-invariant perturbations,” NeurIPS 2024 (link); Zhang et al., "A careful examination of large language model performance on grade school arithmetic," NeurIPS 2024 (link).