- 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:
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Chatbots powered by LLMs
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AI decision-making tools
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Generative AI content platforms
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LLM-based internal tools or assistants
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AI APIs or SaaS platforms
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AI-integrated voice interfaces or mobile apps
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- 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:
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The LLM prompts & outputs
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API endpoints & plugin integrations
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Authentication flows
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Deployment configurations
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Access controls and data handling
Our tests cover both the AI layer and its supporting environment for full-stack security.
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- 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:
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Basic AI app: 1–2 weeks
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Complex LLM integrations or APIs: 2–4 weeks
We provide clear timelines during the scoping phase.
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- What deliverables do I get?
You’ll receive:
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Detailed report of all findings with severity ratings
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Mapped risks to OWASP LLM Top 10
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Evidence of exploitation
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Clear remediation guidance
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Executive summary for stakeholders
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- 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?Can Bluefire Redteam help with secure AI development from the start?
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