HafezSecure

LLM Model Security Assessment

Security assessment of Large Language Models including prompt injection, model extraction, and training data leakage

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About This Service

Our LLM Model Security Assessment tests a large language model at the level of the model itself — its weights, its behavior, and the endpoint that serves it — rather than the application wrapped around it. We test whether the model can be pushed past its safety training (jailbreak and adversarial inputs), whether it discloses sensitive information or the data it was trained on, whether it can be copied or its parameters recovered (model extraction and theft), and whether it stands up to poisoning and malformed input. Because a safety filter tuned for English can be slipped with Persian character variants and mixed script that leave the model's understanding intact, we test the language-specific evasions too. The work follows the OWASP Top 10 for LLM Applications and the OWASP AI Testing Guide, and draws on MITRE ATLAS. Every finding is proven, rated with CVSS alongside its real impact, and written up with a concrete fix.

What's Included

Jailbreak and safety-training bypass: pushing the model past the behavior it was trained to refuse

Direct and indirect prompt injection against the model

Language-specific evasion: character variants, zero-width characters, and mixed-script obfuscation in Persian

Sensitive information disclosure, including memorized training data

Model extraction and model theft through the inference endpoint

Membership inference: whether a given record was in the training data

Adversarial input and robustness under malformed or crafted prompts

Data and model poisoning exposure, where the training or fine-tuning path is in scope

Insecure output handling where the model's output reaches another system

Unbounded consumption and model denial-of-service

Deployment and configuration review of the model endpoint

How It Works

1
Model Discovery & Scoping
We map the model: how it is deployed, how it is reached, whether it is fine-tuned or retrieval-augmented, and what it is trusted to do. This sets the security perimeter and the depth of access for the test.
2
Jailbreak & Injection Testing
We try to push the model past its safety training and to make it follow an injected instruction over its own rules, including the Persian-language evasions that a filter tuned for English misses.
3
Disclosure & Extraction Testing
We test whether the model discloses sensitive information or memorized training data, whether membership in the training set can be inferred, and whether the model can be copied or its parameters recovered through the endpoint.
4
Robustness & Deployment Testing
We test the model against adversarial and malformed input, review the endpoint's authentication, authorization, and rate limits, and check how its output is handled where it reaches another system.
5
Validation, Reporting & Re-testing
Every finding is proven with a reproducible case, rated with CVSS and the real impact, mapped to the OWASP Top 10 for LLM Applications, and written up with a concrete fix. After you remediate, we re-test to confirm the fixes hold.
Deliverables
  • Executive summary with a risk overview for decision-makers
  • Detailed technical report, each finding with a reproducible case
  • Jailbreak, prompt-injection, and language-specific evasion findings
  • Information-disclosure and training-data leakage findings
  • Model-extraction, model-theft, and membership-inference findings
  • Robustness, output-handling, and endpoint deployment findings
  • Findings mapped to the OWASP Top 10 for LLM Applications, with CVSS and business impact
  • Prioritized remediation guidance and re-testing after remediation

Why HafezSecure

Model-Level Depth
We test the model itself — its safety training, what it has memorized, and whether it can be copied — not only the application built on top of it, which is where a model-level weakness would otherwise hide.
Persian-Aware Testing
A safety filter tuned for English can be slipped with Persian character variants and mixed script that leave the model's understanding intact. We test those evasions, which an English-only assessment does not.
Every Finding Validated
Model testing is prone to false alarms; a single lucky output proves nothing. We reproduce every finding before we report it, so what you fix is real.
Reproducible and Actionable
Every finding comes as a case you can replay, mapped to a recognized standard, with a concrete fix for the guardrail, the endpoint, or the deployment.

Frequently Asked Questions

What is an LLM Model Security Assessment?

It tests a large language model at the level of the model and its endpoint — jailbreak and safety-training bypass, prompt injection, information disclosure and training-data leakage, model extraction and theft, adversarial robustness, and deployment security — following the OWASP Top 10 for LLM Applications and the OWASP AI Testing Guide, with each finding proven and rated.

How is this different from an LLM application assessment?

An application assessment tests the whole product built around a model — its prompts, its data sources, its integrations, and the app's own auth. A model assessment tests the model itself: its safety training, what it has memorized, and whether it can be copied. Teams that host or fine-tune their own model need the model-level view; teams that only consume a model through an app usually start with the application assessment.

What is the difference between prompt injection and jailbreak?

Jailbreak is getting the model to ignore its own safety rules — to produce what it was trained to refuse. Prompt injection is getting it to follow an attacker's instruction over the one it was given, often through content it reads. They overlap, and a model assessment tests both.

What access do you need to start?

At minimum, access to the model endpoint and its interface. Where the model is self-hosted, access to the weights, the deployment configuration, and any fine-tuning or training path (a white-box view) lets us test extraction, memorization, and poisoning far more thoroughly. We scope the depth of access with you before we start.

Do you test in black-box, grey-box, or white-box mode?

All three. Black-box reaches the model only through its endpoint, white-box works from the weights and deployment configuration, and grey-box sits in between. Extraction and robustness are meaningful in black-box; memorization, poisoning, and deployment findings need the deeper access, so we match the mode to the questions that matter to you.

Should the assessment run against production or a staging environment?

A staging deployment that mirrors production is preferred, because extraction and denial-of-service testing send a high volume of queries. Where we test against production, we agree on rate and boundary limits first, so the live service is not disrupted.

How are findings rated?

Each finding is scored with CVSS and presented next to its real impact, so a curiosity is not mistaken for a leak of sensitive data or a practical path to copying the model. That pairing is what lets you fix in the right order.

Do you re-test after we fix the findings?

Yes. Re-testing after remediation is part of the engagement: we confirm each fix actually closes the issue and did not weaken the model elsewhere, and the report is updated to reflect the remediated state.

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