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
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.
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.”
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
Focus
AI/ML Pentesting:
- Securing AI-powered systems, especially Large Language Models (LLMs)
Traditional App Pentesting:
- Securing web, mobile, network, or cloud infrastructure
Core Threats
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
Standard Reference
Testing Techniques
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
Environment
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
Attack Surface
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
Risk Examples
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
Mitigation Focus
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.
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.
Seamless integration with Jira
Seamlessly Integrate the platform with Jira cloud.
Real-Time Reporting
Download real-time comprehensive reports and access vulnerability findings, remediation, and references with one click.
You're Partnering with the Best—We've Earned It!
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.
Why is AI Pentesting important?
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.
What types of AI applications need pentesting?
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
What standards do you follow for testing LLMs?
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.
What is prompt injection and why is it dangerous?
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.
How is AI pentesting different from traditional web app pentesting?
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.
Do you test both the AI model and its surrounding infrastructure?
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.
Can Bluefire Redteam test proprietary or fine-tuned models?
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.
How long does an LLM pentest take?
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.
What deliverables do I get?
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
Do you offer retesting after vulnerabilities are fixed?
Indeed. To guarantee that fixes are successful and vulnerabilities are completely fixed, we offer free retesting for all high and critical findings.
Can Bluefire Redteam help with secure AI development from the start?
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?
A checklist can’t save you during a real attack.
But Bluefire Redteam can show you how attackers think, move, and exploit — before it’s too late.