Prompt injection
Attackers manipulate LLM responses by crafting malicious prompts to bypass guardrails or extract sensitive data
Penetration Testing
AI and LLM applications
Test the security and resilience of AI models, large language models (LLMs), and AI-driven applications against adversarial attacks and exploitation techniques.
Discuss an engagement- Objective
- manipulation, extraction and data leakage in AI systems
- Maturity
- models and pipelines already in production
- Approach
- adversarial techniques, manual testing and proprietary tooling
- Result
- resilient models, pipelines and endpoints
Securing the next generation of AI systems
AI systems, particularly large language models (LLMs) and machine learning applications, introduce unique attack surfaces that adversaries can exploit. These models are susceptible to adversarial manipulation, data poisoning, model extraction, and prompt injection attacks.
SilentGrid's AI/LLM Penetration Testing evaluates the security of AI models, uncovering vulnerabilities that could lead to malicious model manipulation, privacy violations, and data leakage. Our engagements focus on securing AI pipelines, model integrity, and deployment environments.
The methodology combines adversarial machine learning techniques with conventional security testing, drawing on the OWASP Top 10 for Large Language Model Applications and MITRE ATLAS. Manual analysis by consultants is paired with customised adversarial tooling, so findings reflect real impact on model integrity, data confidentiality and system availability rather than tool output alone.
Why AI/LLM security is critical
The expanding AI attack surface:
Model extraction
Adversaries attempt to reverse engineer AI models, stealing intellectual property or replicating models for malicious use
Data poisoning
Manipulation of training data to inject bias, backdoors, or malicious behaviours into deployed models
Insecure endpoints
Exposed AI APIs and inference endpoints provide new vectors for unauthorised access and manipulation
Is AI/LLM penetration testing the right fit?
The engagement tests the model, its training data and the pipeline around it, so it suits organisations running AI in production or building models of their own. Where AI is only consumed through a third-party product, application testing of that integration is the more useful question.
AI/LLM penetration testing is essential for
- Organisations deploying LLMs, AI chatbots, or machine learning models in production
- Businesses handling sensitive data through AI models
- Companies developing proprietary AI/ML solutions and seeking to protect intellectual property
- Enterprises integrating AI into customer-facing services or automation pipelines
Key testing areas
SilentGrid leverages a combination of adversarial AI techniques, manual testing, and proprietary tooling to simulate real-world attacks on AI/LLM systems. Our experts assess AI pipelines from data ingestion to model deployment, identifying weaknesses at every stage.
- 01
Prompt injection and output manipulation
- Testing LLMs for prompt injection vulnerabilities that lead to model misalignment, data leakage, or output control
- Evaluating model guardrails for bypass techniques
- 02
Model extraction and theft
- Simulating adversaries attempting to extract model weights, parameters, or training data through API interaction
- Testing for query-based model extraction techniques
- 03
Adversarial input manipulation
- Crafting adversarial inputs that trigger incorrect outputs or misclassification in AI systems
- Evaluating vision-based models for image perturbations and audio-based models for adversarial waveforms
- 04
Data poisoning and model integrity
- Testing the resilience of models against data poisoning attacks during the training phase
- Simulating data manipulation that introduces bias or backdoors into production models
- 05
Model integrity and guardrail bypass
- Testing whether safety and policy guardrails can be circumvented through indirect or chained instructions
- Assessing whether the model's behaviour can be altered in ways its operators would not sanction
- 06
Privacy violations and data leakage
- Probing for disclosure of training data, system prompts or other users' information
- Testing what the model reveals about the systems and data it is connected to
- 07
API and endpoint security
- Assessing inference APIs for improper authentication, excessive permissions, and insecure deployments
- Testing for model abuse, misuse, and unauthorised access to AI pipelines
Benefits of AI/LLM penetration testing
Protect model integrity
Ensure AI models remain free from manipulation, poisoning, or adversarial bias
Secure AI pipelines
Harden the entire AI lifecycle, from data ingestion to deployment
Prevent model theft and extraction
Safeguard intellectual property by detecting model extraction vulnerabilities
Harden inference endpoints
Lock down AI/LLM APIs and inference points to prevent abuse and exploitation
Plan the year
Test the model once, or through the year.
A single AI/LLM test covers prompt injection, data leakage and model manipulation in a model or pipeline in production. Where models and prompts change through the year, an annual program can schedule this testing alongside the rest of the estate and validate releases each month.
Explore annual programsFrameworks behind the testing
Testing is structured against published frameworks so coverage can be discussed in terms both sides recognise, rather than against a private checklist.
OWASP Top 10 for LLM Applications
The reference list of risks specific to applications built on large language models.
MITRE ATLAS
The Adversarial Threat Landscape for Artificial-Intelligence Systems, mapping tactics and techniques used against AI.
Deliverables and reporting
SilentGrid provides in-depth insights into AI/LLM vulnerabilities, showing whether your models and deployment pipelines are resilient against adversarial threats.
Adversarial testing report
Documentation of successful prompt injections, model theft attempts, and adversarial perturbations
Model extraction analysis
Highlighting pathways that lead to partial or complete model extraction
Training data security report
Identifying data poisoning risks and model integrity issues
API/endpoint vulnerability analysis
Assessing AI deployment endpoints for access control weaknesses and misconfigurations
Executive summary
A high-level overview for leadership, summarising the impact of AI/LLM vulnerabilities and recommended mitigations
Consultation and support
Post-assessment guidance to help engineering teams address the findings effectively
Why SilentGrid
SilentGrid's consultants are hand-picked, and between them they have delivered penetration testing globally over decades. Their sector experience covers banking and financial services, insurance, government, critical infrastructure and healthcare, so an assessment is read against how the systems in question are actually run.
Consultants find 0-day vulnerabilities in commercial software and speak or teach at security conferences. That research produces the custom tooling used to reach the flaws automated scanning leaves behind, and it keeps the techniques current. Testing follows concepts set out in NIST, OWASP, PTES and OSSTMM.
CREST ANZApproved company Individual credentials across our team include
- OSEE
- OSCE3
- OSED
- OSEP
- OSWE
- GXPN
- CRTO
- CRTE
- CRTP
- OSCP
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Common questions
What is AI/LLM penetration testing?
AI/LLM penetration testing examines models, LLM applications and their pipelines the way an adversary would: attempting prompt injection and guardrail bypass, model extraction, data poisoning, adversarial inputs, and leakage of training data or system prompts, and testing the inference APIs around them. Manual analysis is paired with customised adversarial tooling, structured against the OWASP Top 10 for LLM Applications and MITRE ATLAS.
What does AI/LLM penetration testing cover?
Prompt injection and output manipulation, model extraction and theft, adversarial input manipulation, data poisoning and model integrity, and the security of inference APIs and endpoints.
What is prompt injection?
An attack where crafted prompts manipulate the model's responses to bypass guardrails or extract sensitive data. Testing covers both the injection itself and whether the model's guardrails can be bypassed.
Can you test whether our model can be extracted?
Yes. We simulate adversaries attempting to extract model weights, parameters or training data through API interaction, including query-based model extraction techniques, and report the pathways that lead to partial or complete extraction.
Do you test the pipeline or only the model?
The whole lifecycle. AI pipelines are assessed from data ingestion through to model deployment, identifying weaknesses at every stage, including the deployment environment itself.
Do you test vision and audio models as well as LLMs?
Yes. Vision-based models are evaluated for image perturbations and audio-based models for adversarial waveforms, alongside adversarial inputs that trigger incorrect outputs or misclassification.
How is the testing performed?
Through a combination of adversarial AI techniques, manual testing and proprietary tooling, used to simulate real-world attacks against AI and LLM systems.
What do we receive at the end?
An adversarial testing report documenting successful prompt injections, model theft attempts and adversarial perturbations, a model extraction analysis, a training data security report, an API and endpoint vulnerability analysis, an executive summary, and post-assessment consultation.
Secure your AI systems
Get started with AI/LLM penetration testing
Fortify your AI models and machine learning pipelines
SilentGrid's AI/LLM Penetration Testing service helps secure cutting-edge AI models and machine learning pipelines, preventing adversarial attacks before they happen.