LLM security testing vs. web pentesting: key differences you can’t ignore

Large language models (LLMs) are rapidly being embedded into applications, customer support systems, and internal tools. While they can process natural language with remarkable accuracy, their integration also introduces new security risks that traditional web application testing doesn’t cover. From input validation flaws to advanced prompt injection, LLM security testing requires a mindset shift—understanding both the similarities to and differences from traditional web app testing. Let’s explore where these two testing disciplines diverge, and what security teams must consider when securing LLMs.

1. Target surfaces: where attacks start

In traditional web app pentesting, the attack surface is well-defined:

• Web applications
• APIs
• Databases
• Authentication mechanisms
• User input fields

For LLMs, the surface is broader and often more unpredictable:

• Model’s API – the primary gateway for interacting with the LLM
• Prompts – the natural language inputs that guide model behavior
• Training data – which may contain sensitive or manipulated information
• Response generation – outputs that could expose data or produce harmful content
• Hidden system prompts – behind-the-scenes instructions controlling the model

Why it matters: While web apps focus on structured inputs, LLMs operate on unstructured natural language, making it harder to anticipate every possible malicious request.

2. Attack goals: what attackers want

The ultimate objective of a security test is to think like an attacker. For traditional web applications, that often means uncovering vulnerabilities such as SQL injection, cross-site scripting (XSS), or CSRF to gain unauthorized access or manipulate data.

In LLMs, the goals shift toward exploiting the model’s unique weaknesses:

• Prompt injection – manipulating model instructions to bypass restrictions
• Data leakage – extracting sensitive training data
• Bias manipulation – altering responses to reflect harmful or skewed views
• Unauthorized access – retrieving restricted information through crafted prompts

This requires testers to focus not only on input/output control but also on how the model interprets and processes instructions.

3. Exploitation methods: how attacks happen

Traditional web pentesting relies on proven methods like:

• Fuzzing
• Exploiting known CVEs
• Privilege escalation
• Session hijacking

LLM security testing involves new, AI-specific exploitation techniques:

• Adversarial prompts – carefully crafted inputs to elicit restricted behavior
• Jailbreak attacks – bypassing model safeguards entirely
• Fine-tuning manipulation – modifying or injecting malicious data during model training
• Indirect prompt injections – embedding malicious instructions in external sources (e.g., websites, documents) that the LLM processes

Because LLMs interpret context, attackers can hide malicious intent inside seemingly benign text—a challenge not seen in traditional structured input attacks.

4. Impact of exploitation: what’s at stake

The consequences of exploitation are also different.
For web apps, impacts include:

• Unauthorized access
• Data breaches
• Website defacement
• Full system compromise
  

For LLMs, potential damage extends beyond typical breaches:

• Private data leakage through prompt injection or model inversion
• Misinformation—deliberate manipulation of model outputs
• Unethical behavior—bypassing content filters
• Compliance violations—especially with GDPR or HIPAA if personal data is exposed
• Supply chain risks from poisoned training data introducing backdoors

Given LLMs’ role in generating and processing information, a single exploit can not only compromise security but also erode trust in the system’s integrity.

5. Methodologies: testing without a universal standard

Web pentesting benefits from established methodologies like:

• OWASP Testing Guide
• OWASP Top Ten
• PTES
• NIST standards

In contrast, LLM security testing has no universally accepted framework—yet. Early initiatives like the OWASP Top Ten for LLMs are emerging, but much of the expertise lies in proprietary techniques developed by specialized security teams.

At Strike, for example, our research team actively investigates novel jailbreaking strategies targeting leading models such as ChatGPT, DeepSeek, and Ngrok. These findings are responsibly disclosed to providers, helping advance collective defenses while keeping critical bypass techniques confidential.