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AI & LLM Application Penetration Testing Services​

Secure Your Artificial Intelligence Systems with Bluefire Redteam’s AI & LLM Penetration Testing Services

Trusted by global organisations for top-tier cybersecurity solutions!

What Is LLM Pentesting?

The process of assessing AI systems based on large language models, or LLM pentesting, in order to find weaknesses like data leaks, prompt injections, unauthorised access, and model misuse.

We simulate real-world attacker techniques to uncover vulnerabilities specific to:

  • Prompt injection attacks

  • Insecure plugin integrations

  • Data leakage via model responses

  • Misalignment and unsafe outputs

  • Training data poisoning

  • Identity impersonation via AI

  • Insecure deployment configurations

AI/ML Pentesting
LLm pentesting

What AI/LLM Applications Should Be Tested?

If your system uses or integrates any of the following, it’s time for a pentest:

  • AI Chatbots (customer service, healthcare, legal, finance, etc.)

  • LLM-integrated web applications

  • Voice assistants powered by LLMs

  • AI agents with internet access

  • LLMs in decision-making systems (HR, insurance, lending, etc.)

  • AI APIs exposed externally

  • AI-powered document analysis or summarization tools

Why Choose Bluefire's AI Security Testing Services?

At Bluefire Redteam, we’ve built our reputation on real-world, advanced penetration testing. Here’s how we apply it to AI/LLM testing:

Specialized AI Pentesters

70% of breaches occur due to ongoing vulnerabilities—stay protected with our continuous testing approach

Custom Threat Modelling

Whether your AI use case involves public-facing bots, internal AI agents, or B2B APIs, we customise threat models for it.

End-to-End Coverage

We assess everything—from prompt injection testing, API endpoint security, to model configuration audits and plugin vulnerabilities.

Trusted by Customers — Recommended by Industry Leaders.

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CISO, Microminder Cyber Security, UK

“Their willingness to cooperate in difficult and complex scenarios was impressive. The response times were excellent, and made what could have been a challenging project, a relatively smooth and successful engagement overall”

CEO, IT Consulting Company, ISRAEL

“What stood out most was their thoroughness and attention to detail during testing, along with clear, well-documented findings. Their ability to explain technical issues in a way that was easy to understand made the process much more efficient and valuable.”

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IT Manager, Nobel Software Systems, INDIA

“The team delivered on time and communicated effectively via email, messaging apps, and virtual meetings. Their responsiveness and timely execution made them an ideal partner for the project.”

AI Pentesting vs. Traditional Pentesting: What’s the Difference?

AI-specific vulnerabilities are overlooked by traditional pentesting. To find hidden risks that only become apparent under natural language-based attacks, you need a specialised AI pentesting approach if your application uses LLMs like ChatGPT, GPT-4, Claude, or LLaMA.

AI/LLM Pentesting vs. Traditional Application Pentesting

AI/ML Pentesting:

  • Securing AI-powered systems, especially Large Language Models (LLMs)

 

Traditional App Pentesting:

  • Securing web, mobile, network, or cloud infrastructure

AI/ML Pentesting:

  • Prompt injection
  • Model manipulation, output abuse
  • Data leakage
  • Insecure plugin access
  • Training data poisoning

Traditional App Pentesting:

  • SQL injection
  • XSS
  • SSRF
  • Authentication bypass
  • Insecure APIs

AI/ML Pentesting:

Traditional App Pentesting:

AI/ML Pentesting:

  • Prompt injection
  • Model manipulation, output abuse
  • Data leakage
  • Insecure plugin access
  • Training data poisoning

Traditional App Pentesting:

  • SQL injection
  • XSS
  • SSRF
  • Authentication bypass
  • Insecure APIs

AI/ML Pentesting:

  • Natural Language Inputs, API integrations with LLMs, fine-tuned/custom models

Traditional App Pentesting:

  • Web servers, databases, front-end/back-end applications

AI/ML Pentesting:

  • LLM prompts, training data, user instructions, plugin or tool access, and backend integrations.

 

Traditional App Pentesting:

  • HTTP parameters, form inputs, session tokens, endpoints

AI/ML Pentesting:

  • LLM discloses sensitive data, executes unauthorized functions, produces harmful or biased content

Traditional App Pentesting:

  • Database dumps, unauthorized access, data breaches, site defacements

AI/ML Pentesting:

  • Prompt validation
  • Access restrictions
  • Output monitoring
  • Model behaviour tuning

Traditional App Pentesting:

  • Input validation
  • Access control
  • Secure coding
  • WAFs

PentestLive - Our In-House Penetration Testing As A Service Platform

Effortlessly manage vulnerabilities with our real-time system. Transition vulnerabilities from “open” to “in progress” to indicate active patching, and move them to “verification” for thorough checks.

Our centralized dashboard provides immediate insights into your security posture, featuring a risk meter, real-time activity feed, and detailed vulnerability statistics. Plus, generate and download assessment reports effortlessly.

Real-Time Vulnerability Management

Effortlessly manage findings: moving a vulnerability from “open” to “in progress” shows active patching, while transitioning to “verification” prompts a patch check.

dashboard

Immediate Security Insights

The dashboard centralizes all relevant security metrics, providing security teams with immediate insights into their current security posture. The current risk meter, real-time activity feed, and vulnerability statistics offer a real-time snapshot of the organization’s security landscape.

Vulnerability Dash

Seamless integration with Jira

Seamlessly Integrate the platform with Jira cloud.

Vulnerability Dash

Real-Time Reporting

Download real-time comprehensive reports and access vulnerability findings, remediation, and references with one click.

Vulnerability Dash

You're Partnering with the Best—We've Earned It!

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Frequently Asked Questions (FAQs) — AI & LLM Penetration Testing Services

What is LLM Pentesting?

Penetration testing of applications developed with Large Language Models is known as LLM pentesting. In order to secure your AI application, it entails detecting vulnerabilities such as data leakage, model abuse, prompt injection, and insecure output handling while mimicking actual attack methods.

Because AI systems and LLM applications are very dynamic and frequently handle sensitive data, they are vulnerable to non-traditional threats. Your app might create dangerous content, leak data, or be manipulated by hackers if it isn’t properly tested. AI pentesting guarantees that your system is resilient to these kinds of attacks.

We recommend AI security assessments for any application using:

  • Chatbots powered by LLMs

  • AI decision-making tools

  • Generative AI content platforms

  • LLM-based internal tools or assistants

  • AI APIs or SaaS platforms

  • AI-integrated voice interfaces or mobile apps

The most important security threats in LLM-based apps are listed in the OWASP Top 10 for Large Language Model Applications, which we adhere to. This covers risks such as Training Data Poisoning, Insecure Plugins, and Prompt Injection.

By creating malicious input prompts, attackers can use the prompt injection technique to alter the LLM’s output. In integrated systems, this may result in command execution, output manipulation, or even illegal data access. In AI pentesting, it is among the most dangerous threats.

Since LLM applications use natural language for interaction, they are more susceptible to various attack vectors than traditional apps, such as malicious prompts, model hallucinations, or overly permissive plugin access. Beyond the OWASP Web Top 10, AI pentesting calls for specific methods catered to LLM behaviour.

Yes. We evaluate:

  • The LLM prompts & outputs

  • API endpoints & plugin integrations

  • Authentication flows

  • Deployment configurations

  • Access controls and data handling

Our tests cover both the AI layer and its supporting environment for full-stack security.

Of course. We can modify our testing process to mimic threats unique to your unique deployment, regardless of whether you use OpenAI, Anthropic, open-source LLMs like LLaMA or Mistral, or your own refined models.

It depends on complexity, but typically:

  • Basic AI app: 1–2 weeks

  • Complex LLM integrations or APIs: 2–4 weeks
    We provide clear timelines during the scoping phase.

You’ll receive:

  • Detailed report of all findings with severity ratings

  • Mapped risks to OWASP LLM Top 10

  • Evidence of exploitation

  • Clear remediation guidance

  • Executive summary for stakeholders

Indeed. To guarantee that fixes are successful and vulnerabilities are completely fixed, we offer free retesting for all high and critical findings.

Indeed. We provide Secure AI Design Consulting in addition to pentesting to assist you in integrating security into your AI systems from the start, minimising risk exposure and preventing expensive rework.

Ready for the Ultimate Security Test?

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But Bluefire Redteam can show you how attackers think, move, and exploit — before it’s too late.

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