Services AI/LLM Penetration Testing
A Large-Language Model (LLM) security assessment involves targeting systems that do natural language processing, including but not limited to chatbots, language models, and other AI-driven interfaces. Testing is based on the OWASP Top 10 for LLMs and is continuously enhanced to incorporate the latest developments in LLM security.
What's included:
Demonstrated impact to help with executive and developer understanding
Comprehensive reporting with detailed step-by-step instructions to reproduce
Advisory on remediation steps and retesting to validate closure of findings
Contact Us
What's included:
Demonstrated impact to help with executive and developer understanding
Comprehensive reporting with detailed step-by-step instructions to reproduce
Advisory on remediation steps and retesting to validate closure of findings
Service Highlights
Prompt Injection
Attempt to coerce LLM output into unintended actions.
The Packetlabs Commitment
In-Depth Remediation Guidance
Clear, business-impact-focused reporting and remediation guidance.
0% "Audit Noise"
Proof-of-concept attacks that demonstrate risk.
Full App Coverage
Coverage for plugins, third-party APIs, and user input channels.
Expert Insights
Expert insights from certified offensive security professionals (OSWE, OSCP, Burp, OSED.)
Why Invest in AI/LLM?
Correct Overreliance on Automation
Spot scenarios where trust in AI output could mislead teams, customers, or systems.
Ward Against Supply Chain Vulnerabilities
Examining connected tools and dependencies for weak links that could be exploited.
Protect Against Training Data Poisoning
Verify that inputs used to train or fine-tune your model cannot be compromised.
Identify Real-World Attack Vectors
Look for ways that model responses could lead to real-world web attacks (XSS, CSRF, SSRF.)
Resources
Pentest Sourcing Guide
Download our Pentest Sourcing Guide to learn everything you need to know to successfully plan, scope, and execute your penetration testing projects.
Download GuideRelated Posts
May 05 - Blog
Tactics for Jailbreaking LLMs
LLM security risks grow with advanced jailbreak techniques like Many-shot, Deceptive Delight, and PAIR. Discover how attackers bypass AI safety measures and how to mitigate these emerging threats.
March 26 - Blog
How LLMs Are Enabling Automated Vulnerability Discovery
How LLMs enable automated vulnerability discovery is top of mine for many security experts. Today, we define fuzz testing and describe the benefits of fuzzing regarding software supply chain security.
May 02 - Blog
Using LLMs Like ChatGPT To Support OSINT Campaigns
Explore how LLMs such as ChatGPT can enhance OSINT operations from data gathering to deep analysis, boosting speed and intelligence quality in security operations.
