Our AI strategy for preemptive security
Originally posted April 2024 and updated in June 2026.
Bugcrowd has launched AI Penetration Testing services with pen testers to help organizations secure Large Language Model (LLM) applications and AI systems. As AI becomes mainstream, new vulnerabilities like prompt injection and data poisoning are emerging, and organizations must proactively test and secure these systems to protect user data and maintain trust. As more organizations connect Large Language Model applications to web applications, web apps, APIs, and internal workflows, AI penetration testing helps security teams validate both traditional vulnerabilities and AI-specific exploitation paths before attackers find them.
That shift makes security testing and a more specialized penetration test approach increasingly important for AI-powered applications, especially when failures can create real business impact.
Ai Pentesting Key Insights:
As access to AI technology becomes more widespread, organizations in every industry are adopting these cutting-edge technologies across customer-facing products, internal tools, web applications, and web apps. However, as AI technology continues to be rapidly commercialized, new potential security vulnerabilities are quickly being surfaced. AI penetration testing gives security teams a practical way to assess these risks across models, prompts, API keys, embedded workflows, Tool Calling, and connected data sources before they create business impact.
There’s no better way to understand the potential severity of vulnerabilities in an AI system than the ethical hackers who are testing these AI-powered tools and systems every day. Joseph Thacker, aka rez0, is a security researcher who specializes in application security and AI. We asked him to break down the current landscape of new vulnerabilities specific to AI.
“Even security-conscious developers may not fully understand new vulnerabilities specific to AI pentesting, such as prompt injection, so doing security testing on AI features is extremely important. In my experience, many of these new AI applications, especially those developed by startups or small teams, have traditional vulnerabilities as well. They seem to lack mature security practice making pentesting crucial for identifying those bugs, not to mention the new AI-related vulnerabilities that a modern penetration test is designed to uncover.
Naturally, smaller organizations will have less security emphasis, but even large enterprises are moving very quickly to ship AI products and features, leading to more detection of vulnerabilities than they would typically have. Since Generative AI applications handle sensitive data (user information and often chat history), as well as often making decisions that impact users, pentesting is necessary to maintain trust and protect user data.
Regular pentesting of AI applications helps organizations stay ahead as the field of AI security is still in its early stages and new vulnerabilities are likely to emerge,” rez0 said.
To learn more about AI pen testing, check out the blog AI Deep Dive: Pen Testing.
As organizations increasingly adopt large language models (LLMs) to enhance productivity, automate tasks, and drive innovation, it is imperative to acknowledge the potential vulnerabilities associated with their use.
One of the primary concerns is data privacy, as LLMs require vast amounts of data to function effectively, potentially exposing sensitive or confidential information.
These models are susceptible to bias, reflecting and perpetuating the prejudices present in their training data, which can lead to unfair or discriminatory outcomes.
The reliance on LLMs can create security risks, as malicious actors might exploit these systems through adversarial attacks, adversarial inputs, or by crafting inputs that manipulate the model’s behavior and increase the risk of data exfiltration. The black-box nature of LLMs also poses interpretability challenges, making it difficult for organizations to fully understand how decisions are made, which complicates accountability and governance.
Implementing robust risk management strategies for the discovery of vulnerabilities with AI Pen testing are crucial to mitigating threats such as prompt injection, model poisoning, and multi-step exploit chains.
AI pentesting tools can support faster discovery, repeatable security reviews, and broader test coverage across Large Language Model applications. These tools may include vulnerability scanners, prompt injection test harnesses, payload libraries, attack-surface map utilities, and workflows for reviewing API keys, Tool Calling, embedding providers, vector embeddings, and connected data stores such as pgvector.
However, tooling alone is not enough. Many AI risks depend on application context, user permissions, data access, business logic, and how the model interacts with external systems. For example, a vulnerability scanner may identify exposed endpoints, but a skilled tester is better positioned to validate whether those endpoints can be chained into a proof of exploit. This is especially important for AI-integrated workflows where a model can retrieve data, call tools, trigger actions, or expose sensitive information through indirect prompts.
For security teams, the strongest approach is to combine AI pentesting tools with human-led testing. Automated testing can help with continuous attack-surface discovery, while experienced pentesters can validate exploitation paths, business logic flaws, unsafe Thinking Modes, and real-world abuse cases that require adversarial creativity.
AI applications change quickly, which means security testing should not be limited to a single point-in-time review. When teams are shipping new prompts, model integrations, Tool Calling logic, retrieval workflows, or provider changes, AI pentesting should be considered as part of the broader CI/CD pipeline.
This does not mean every release needs a full manual pentest. Instead, security teams can use a layered model:
This approach helps organizations move faster without treating AI security as an afterthought. It also gives teams a clearer path for connecting AI security reviews to existing application security and release-management processes.
Bugcrowd AI Pen Tests help organizations uncover the most common application security flaws and AI-specific attack paths using a testing methodology based on our open-source Vulnerability Rating Taxonomy (VRT), with approaches that align to AI red teaming frameworks such as MITRE ATLAS and the OWASP Top 10 for LLMs
All AI Pen Tests include:
AI penetration testing is the process of evaluating the security of AI systems, including applications like chatbots and machine learning models. It aims to identify vulnerabilities that could lead to unauthorized access, data breaches, or operational disruptions.
AI pentesting tools are technologies that help security teams test AI applications, Large Language Model workflows, and related infrastructure. These may include vulnerability scanners, prompt injection testing tools, attack-surface discovery tools, payload libraries, docker compose testing environments, and utilities for reviewing API keys, Tool Calling permissions, embedding providers, vector embeddings, and Knowledge Graph connections. These tools can improve coverage, but they should be paired with experienced testers who can validate real-world impact.
No. AI pentesting tools can help identify common issues and support repeatable testing, but manual pentests are still important for validating business logic flaws, multi-step exploitation paths, and proof of exploit scenarios. Human testers can evaluate context, user roles, sensitive workflows, and the business impact of a vulnerability in ways that automated tools cannot fully replicate.
As AI systems become more integrated into business operations, they process sensitive data and make critical decisions. Penetration testing helps organizations identify and mitigate risks associated with these systems, maintaining user trust and safeguarding sensitive information. A penetration tester can utilize AI tools in order to help deliver faster and more reliable threat intelligence and security testing results.
Common vulnerabilities in AI systems include:
AI penetration testing should be conducted by experienced security professionals with a background in both cybersecurity and AI technologies. This includes ethical hackers, security researchers, and firms specializing in AI security.
Organizations should test AI applications regularly because models, prompts, integrations, and data flows can change quickly. Quarterly or semi-annual testing may be appropriate for many organizations, while higher-risk systems may need additional testing after major releases, provider changes, new Tool Calling workflows, or updates to the CI/CD pipeline.
The process typically involves:
Traditional penetration testing focuses on conventional applications, systems, APIs, and infrastructure. AI penetration testing includes those areas, but also evaluates AI-specific risks such as prompt injection, unsafe Tool Calling, model behavior, exposed API keys, data leakage, embedding providers, Knowledge Graph access, and business logic flaws in AI-powered workflows.
Organizations should seek services that:
The international AI systems standard, ISO/IEC 42001, outlines requirements for managing AI technologies within organizations. This standard emphasizes security throughout the entire lifecycle of AI systems, addressing the unique challenges associated with AI, including ethical considerations and continuous learning.
Organizations can stay informed by:
Security teams should scope an AI penetration test by identifying the application, model integrations, data sources, user roles, APIs, Tool Calling workflows, CI/CD pipeline, and third-party providers involved. The scope should also include how the AI feature connects to web applications, web apps, Knowledge Graph systems, embedding providers, and sensitive business processes. A clear scope helps pentesters focus on realistic exploitation paths and deliver more actionable findings.
With Bugcrowd AI Pen Tests, your organization can expect the same caliber and quality of testing that has made us an industry leader. Our CrowdMatch technology means you’ll be paired with pentesters with experience in testing AI applications, which is not a common skill among pentesters at other providers.
Your organization can start your pen test in as little as 72 hours. Learn more and access a decade of vulnerability intelligence from the Bugcrowd Platform in every pen test engagement.
Here are some additional resources: