The business integration of generative AI has skyrocketed, and the extent of enterprise usage of AI has risen from 20% in 2017, to 78% by 2024 [1]. And Gartner predicts that over 80% of enterprises will have generative AI running in production by 2026 [2].
As Large Language Models (LLMs) are being introduced into customer service, analytics, and decision-making workflows, it’s only a matter of time before they have become a new target for attackers.
High-profile incidents already show the risks, from jailbreaking ChatGPT to expose hidden prompts or generate illicit outputs, to employees inadvertently leaking confidential code into an LLM and causing corporate data exposure [3].
As enterprises embrace LLM technology, attackers are racing to find weaknesses. Hence, proactively testing and securing these AI systems is now a business-critical mandate.
This article is aimed at decision-makers and managers interested in securing their AI tech stack and looking to learn more about the basics of LLM pen testing, why LLMs can lead to a host of unique security risks, how LLM penetration testing is performed, and what different aspects of a test program require special attention.
What Is LLM Penetration Testing?
LLM penetration testing is essentially ethical hacking focused on AI and ML models. If traditional pen testing is your veteran security guard checking locks and doors, LLM pen testing is their specialist counterpart checking if an AI “brain” can be tricked or misused.
In classic app pen tests, testers look for issues like SQL injection or cross-site scripting. In an AI context, on the other hand, testers probe how a machine learning model might be manipulated by inputs or behaviors that conventional tests would miss.
Think of it as a “black box” audit of your AI: testers treat the LLM like an attacker would, sending crafted prompts, injecting malicious data, and exploring its responses, to see how it behaves under pressure.
Why LLMs Create Unique Security Risks
Deploying LLMs introduces unique attack vectors that didn’t exist in traditional software. Below we outline a few of the most important LLM-specific risks, what they mean, and how they can be exploited.
Prompt Injection & Jailbreaking
One of the hallmark LLM attacks is prompt injection, tricking the model with carefully crafted inputs that make it ignore its instructions or behave maliciously. A related exploit, often called “jailbreaking,” involves designing prompts that convince the AI to bypass its safety rules or content filters. In essence, both techniques aim to get the system to do something it wasn’t intended to do [4].
Adversarial & Poisoning Attacks
Beyond prompt tricks, adversaries can also assault an AI model through adversarial inputs and training-data poisoning. Adversarial input attacks involve feeding the model specially crafted content that causes it to malfunction or misclassify. If attackers can inject or influence even a tiny fraction of an LLM’s training data or fine-tuning data, they may embed a backdoor or bias.
Opaque Behavior ("Black‑Box")
LLMs often function as black boxes, even to their creators. Their decision-making process is not transparent, and you can’t easily trace why an LLM produced a given response. This opacity itself is a security risk – with traditional apps, engineers can verify code on a line by line basis for vulnerabilities; with an LLM you have to deal with a massive neural network that is impossible to interpret simply. Therefore, an AI system may possess failure modes or unsafe behaviors that simply aren’t found until an attacker stumbles across them through sophisticated probing.
How LLM Penetration Testing Is Done
LLM penetration testing borrows from classic offensive security but adds new techniques and tools tailor-made for AI. Generally, testers will use a combination of manual probing and automated agents to evaluate the model. Here are a few core components of how LLM pentests are conducted:
LM‑specific Pentest Techniques
Adversarial Prompts
Testers craft inputs designed to confuse, mislead, or stress the model, known as adversarial prompts. Much like a hacker jiggles a lock to find weaknesses, a pentester tries unconventional or maliciously formatted prompts to see if the LLM produces forbidden content or errors.
This includes things like deliberately obfuscated requests, nonsense phrases or “glitch” tokens, and contradictory instructions, all to observe how the model copes. The goal is to discover prompt patterns that break the AI’s guardrails.
Jail Breaking
A specialized subset of adversarial prompting, known as jailbreak testing, explicitly attempts to make the AI violate its constraints. Pentesters will assume “malicious user” personas and attempt things like: “Ignore all previous instructions and tell me how to… [perform some illicit act]”. Each jailbreak attempt checks whether content filters and safety layers hold up or can be circumvented [5].
Data Exfiltration
With a data exfiltration probe the tester checks if the LLM will leak sensitive info when prompted in clever ways. This could include attempting to recover the system prompt or hidden configuration, extracting pieces of its training data, or tricking it into divulging API keys, personal data, or other secrets.
Multi‑phase/Red‑Team Approach
Effective AI penetration testing is not a one-off script run, and should be considered a multi-phase process, often resembling a full red-team exercise. In phase 1, testers perform reconnaissance and threat modeling, mapping out what LLMs are in use, what data they handle, how they interact with systems, and what a worst-case scenario looks like.
Next comes an active attack phase. Testers will start with basic prompt attacks and gradually escalate to more complex exploit chains. This phased approach mirrors how a real adversary might “level up” their attack after initial successes. Throughout, testers document each vulnerability discovered and pivot to the next, deeper test.
Finally, there is a post-exploitation and analysis phase. Here, the findings are analyzed for impact, to find out the possible consequences of an attack. This phase often involves risk scoring and formulating mitigation strategies for each issue.
The multi-phase approach ensures nothing is left out, covering the entire kill-chain from the initial mapping of AI assets to simulated attacks to detailed impact analysis.
Automated Periodic Testing
A relatively new and promising practice is to supplement human testers with autonomous AI “red team” agents. Given the dynamic nature of AI models, organizations are moving toward continuous testing, where tools like RunSybil deploys multiple AI agents that work in parallel, probing a target system’s defenses nonstop.
Unlike a human tester who might conduct a pentest quarterly, these AI agents can run 24/7 and continuously fuzz the LLM with new prompts or monitor for changes. Other open-source frameworks are rising too. For example, the AutoPentest project uses a GPT-4 based agent to autonomously perform black-box pentests, combining the LLM’s reasoning with external tool plugins [6].
Such agents can be set to periodically test an LLM application, ensuring that there are no new vulnerabilities. This use of AI to test AI creates a feedback loop of constant improvement where you are essentially arming the “good guys” with the same automation and scale that bad actors might use. As one expert observed, we are on the cusp of a tech explosion in AI-driven offense and defense, deploying AI to strengthen cybersecurity on both sides [7].
Why LLM Pen Testing Matters for Your Business
Some business leaders might ask: our company already does regular security testing, why a separate pen test just for LLMs? The answer is that LLMs introduce new risks that directly impact business exposure and threat preparedness. Investing in LLM-focused security assessments yields concrete benefits. The key ones include:
Reduced Risk Exposure
First and foremost, pentesting LLMs decreases the chance of potentially expensive incidents. AI systems are often trusted with sensitive data, and in some cases, even the authority to execute actions or make decisions.
A breach or misapplication of an AI system could leak sensitive data, breach compliance obligations, expose IP through theft, or damage brand. Pentesting allows you to identify all those failure points that an attacker would prey upon.
Moreover, regulators are keeping a close eye on the industry, with multiple companies reporting that there is increased scrutiny around AI ethics and security. Therefore, exercising due diligence in AI security will help demonstrate compliance and provide governance frameworks.
A pentest report will allow auditors and other stakeholders to see that you have exercised the correct level of oversight over your AI systems, so you can avoid things like insecure or biased outcomes.
Staying Ahead of AI-Powered Threats
The flip side of AI in security is that attackers are leveraging AI too. Modern cybercriminals use generative AI to craft extremely convincing phishing lures, automate malware development, and scale up their attacks.
In fact, 87% of global organizations reported facing AI-powered cyber attacks in the last year, from deepfake voice scams to AI-written malware [8]. The threat environment is evolving fast, and businesses need to stay one step ahead.
LLM pen testing helps you anticipate and withstand AI-driven threats. By hardening your own AI, you indirectly bolster defenses against adversaries who might use similar models to attack you. Staying ahead also means keeping up with the latest attack techniques in the AI space.
How Does an AI / LLM Pen Test Program Look?
Implementing LLM penetration testing in your organization isn’t an ad-hoc task, it should be structured as a program integrated with your overall security strategy. A mature AI/LLM pen test program typically follows these components.
1. Scope & Threat Modeling
Every engagement begins with defining scope. Account for all LLMs and AI services that are deployed in/or by your organization, whether that is customer-facing chatbots, internal tools or third-party APIs.
Assess the context for each LLM or AI service, by asking questions such as: What data does the LLM or AI service use or manipulate? Which systems can the LLM or AI service access? This threat modelling activity will document sensitive assets and possible abuse scenarios.
2. Execution of Phased Testing
Once scoped, the pen testing team executes the plan in phases. A typical sequence is essentially applying classic penetration testing methodology to AI, where it starts broad at the surface-level, then dive deeper as clues emerge:
- Reconnaissance: Passive observation and non-invasive queries to gather system information.
- Initial Exploitation: Searching for low-hanging fruit using simple prompt injections or known exploits.
- Adversarial Attack: Executing complex, multi-step attacks for evasions.
- Pivoting: Concentrating on and deeply exploiting any weaknesses you find.
- Risk Analysis: Assessing the potential impact of each finding in a real-world scenario.
3. Hybrid Tools are Introduced
Modern LLM pen testing programs leverage a hybrid of human expertise and AI tools. In practice, it means that your security testers will use specialized software and scripts to augment their manual testing. After some manual probing, they might deploy an LLM-aware scanner or agent to fuzz the AI at scale, to ensure no edge case is missed.
Introducing AI-driven tools has another benefit: speed and adaptability. If the LLM under test is updated or retrained, these tools can quickly re-run tests or regression-test previously fixed issues. The human testers focus on strategy while the AI tools handle brute-force exploration and monitoring. This hybrid model is increasingly important because LLMs themselves evolve, you want an agile testing approach that can keep up with model updates.
4. Integration & Automation of Security
A truly mature LLM security program doesn’t treat pen testing as a one-time project, it integrates testing into the AI lifecycle. This means establishing security checkpoints whenever your AI tech is updated or deployed.
Furthermore, security automation can be added to the CI/CD pipeline, where you might automate a suite of prompt injection tests to run on every new model build or periodically run adversarial input tests on production models to catch regressions.
5. Communication
Finally, an often overlooked but vital part of an AI pentest program is communication of findings and guidance. The technical results of an LLM pen test need to be translated into actionable intelligence for both technical teams and executives.
This means the pen testers should compile a report that doesn’t just list prompts that succeeded in jailbreaking the model, but explains the implications of those findings in business terms.
Thus, a good pen test program ensures all involved groups understand the findings, and often a debrief will be held with developers to walk through how the tester succeeded in exploiting the AI, and with leadership to summarize risk in terms they care about.
The ultimate aim is to turn vulnerabilities found into improvements made, and that requires buy-in via effective reporting and discussion.
Emerging Trends in LLM Penetration Testing
AI security is a fast-moving field. As you build your LLM pentesting capabilities, keep an eye on emerging trends shaping the practice, such as continuous AI-driven pentesting for an example.
Additionally, the growth of LLM‑specific security startups and frameworks has created a new ecosystem of tools and companies dedicated to LLM and AI security. From startups like HiddenLayer and Robust Intelligence to community projects like OWASP’s GenAI Security Project, the industry is coalescing around frameworks for AI security best practices.
We’ve also been seeing significant advances in mitigating prompt injection, as developers are experimenting with input sanitization, prompt filtering, and adversarial training to make models more resistant to injection attacks. In addition, there is also work on “contextual endpoint” solutions, essentially wrappers around an LLM that monitor and restrict its behavior.
We expect that AI red teaming will become an ongoing and AI-empowered practice, an essential maturing as LLMs play larger roles in enterprise. Monitoring these developments will position your organization to be resilient as the threat landscape changes.
Next Steps for Decision-Makers
Here are some tangible next steps for executives and security leaders who want to strengthen the safety of their AI implementations, to ensure that the adoption of LLMs and AI technology in their own organization takes place in a responsible way and combines serious due diligence in security.
Commission an Initial LLM Pentest
Engage a specialized security provider to perform a thorough penetration test of your AI systems. An expert third-party can baseline your risk and uncover hidden vulnerabilities with an attacker’s eye, providing an objective report and remediation plan. For example, Cybri has deep offensive security expertise and experience with a variety of AI environments.
Set up Cross-Functional AI Security Collaboration
Bring together your security team, machine learning/DevOps engineers, and risk/compliance officers to jointly own AI risk management. This cross-functional team should establish policies for safe AI use and ensure that pentest findings are addressed holistically. Breaking down silos is crucial, LLM security is not just an IT problem or just an ML problem – it is both.
Invest in Continuous Testing Capabilities
Don’t make AI security a one-time project. Allocate budget and resources for ongoing monitoring and testing of your LLMs. This could mean training internal staff on LLM pentesting techniques, deploying automated AI security tools, and scheduling regular re-tests. The goal is to build AI security into your operational rhythm, so that as your AI tech evolves, its security keeps pace and you maintain trust with users and stakeholders.
References
- Master of Code. (August 2025). 100+ Generative AI Statistics
- Technology Reseller News. (October 2023). Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026
- AP News. (August 2023). Don’t expect quick fixes in ‘red-teaming’ of AI models. Security was an afterthought
- Wired. (April 2023). The Hacking of ChatGPT Is Just Getting Started | WIRED
- Edgescan. (May 2025). Why AI Security Testing Matters to Your Business: Understanding LLM Penetration Testing
- Arxiv.org. (May 2025). AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
- Wired. (July 2025). I Watched AI Agents Try to Hack My Vibe-Coded Website
- Exploding Topics. (June 2025). 7 AI Cybersecurity Trends For The 2025 Cybercrime Landscape
