Overview: LLM Pentesting Covers
- LLM pentesting is a security discipline tailored to the unique, probabilistic attack surfaces of language models like prompt injection and embedding inversion
- It addresses enterprise vulnerabilities traditional web app tests miss, such as vector-store poisoning, ACL bypass, and semantic-ranking exploitation
- The blog outlines a step-by-step pentesting process including system mapping, log analysis, attack simulation, and patch validation to prevent regression
- Tools like Knostic strengthen LLM defenses with automatic leak detection, guided remediation, prompt-level tracking, and dashboards that track changing risk
- Readers should gain a foundational framework for securing AI systems against fast-evolving threats across search connectors, retrieval pipelines, and model outputs
What Is LLM Pentesting
LLM pentesting is a form of penetration testing tailored to large language models (LLMs), where security professionals simulate real-world attacks to find and fix vulnerabilities in how the model processes prompts, data, and outputs.
Unlike traditional web app testing, which targets static, rule-based systems, LLM pentesting must address dynamic behaviors driven by natural language, embeddings, and probabilistic outputs. This complexity is compounded by a widespread lack of transparency. Most LLM developers, as revealed by Stanford’s 2024 Foundation Model Transparency Index, do not fully disclose their data sources, safety practices, or testing methods (like prompt injection testing or LLM security testing). This makes it difficult for enterprises to assess their systems’ resilience. Adding to the urgency, the 2024 OWASP Top 10 for LLM Applications exposes growing risks like data and model poisoning, underscoring the critical need for robust and targeted security assessments.
Enterprise AI Search Attack Surface
Nowadays, enterprise AI search systems are under attack in numerous ways. They target LLM pipelines, vector stores, access layers, and prompt handling. Below, we'll outline some of the most common attacks that you should be aware of.
Index & vector-store poisoning
Index poisoning is when attackers inject bad data into search indexes or vector databases. This can change search results or corrupt embeddings. Anthropic's 2024 study showed that LLMs can maintain deceptive behaviors even after undergoing standard safety training techniques, such as supervised fine-tuning and reinforcement learning. The study found that these backdoor behaviors were most persistent in larger models and those trained to produce chain-of-thought reasoning about deceiving the training process. This persistence remained even when the chain-of-thought was distilled away, suggesting that current safety training methods may be insufficient to eliminate such deceptive strategies.
The OWASP Top 10 for LLM Applications 2025 identifies data and model poisoning as significant risks, and also emphasizes the need for resilient security measures during the training and deployment of LLMs. In addition, a study by Trend Micro showed how attackers could exploit vector store poisoning in AI systems. By injecting malicious data into vector databases, they manipulated AI outputs, leading to potential data theft and fraudulent activities.
In general, vector-store poisoning is dangerous because it compromises the retrieval layer beneath the LLM . Because it operates outside the prompt boundary, it evades traditional defenses like prompt filtering, model alignment, and fine-tuning. Once poisoned embeddings are seeded into the vector index, they persistently influence similarity search, injecting attacker-controlled content into the LLM’s context window. It can also affect RAG pipelines, incorporating biases or vulnerabilities into responses. Once bad vectors are in, they distort everything pulled into the LLM context window.
ACL bypass via search connectors
Access control lists (ACLs) define what users can see by specifying which files, records, or data sources each user or group is allowed to access within a system. But AI search connectors sometimes bypass these controls.
For example, connecting SharePoint, Jira, or Confluence to an LLM without proper ACL alignment can leak restricted data. An incident reported by ITPro in 2024 highlighted a case where misconfigured ACLs in enterprise LLM servers led to unintended data exposure. Gartner’s 2024 ‘Hype Cycle for Artificial Intelligence,’ emphasizes the importance of aligning AI initiatives with business objectives to mitigate potential risks.
Semantic-ranking manipulation
Semantic-ranking systems sort search results by meaning, not just keyword match. Attackers can exploit this by injecting content,that pushes malicious or self-serving results to the top. A 2024 Harvard University study shows that strategic text sequences can influence the ranking of products in LLM generated recommendations. The study found that adding optimized text sequences to product information can elevate a product's position in the recommendation list, even if it doesn't align with the user's original criteria.
Another study introduces an adversarial attack method that subtly alters prompts to manipulate LLM product recommendation rankings without introducing detectable anomalies. The research highlights significant vulnerabilities in LLM-based recommendation systems to such manipulations.
Finally, a case study published on Medium in 2024 explores how semantic and vector search integration in legal Q&A systems could be manipulated. By strategically crafting inputs, attackers influenced the semantic ranking algorithms, causing the system to prioritize misleading information.
Prompt-born data leakage
Prompt-born data leakage occurs when LLMs unintentionally disclose sensitive information due to crafted prompts. These prompts can manipulate the model into revealing confidential data, such as personal details or proprietary information.
An example is the "Imprompter" attack, where researchers showed that LLMs could be tricked into extracting and sending personal data to unauthorized parties. This attack used hidden prompts that appeared benign but contained hidden instructions, leading to an 80% success rate in data extraction during tests on models like LeChat and ChatGLM (source).
Further studies have shown that LLMs can leak information from their system prompts or previous interactions. For instance, research presented at EMNLP 2024 showed how adversarial prompts could cause models to disclose internal instructions or user data accidentally. These vulnerabilities underscore the importance of implementing resilient security measures, such as input validation, output monitoring, and user training, to mitigate the risks associated with prompt-born data leakage.
Research from HiddenLayer detailed different prompt injection attacks on LLMs, including incidents where models accidentally leaked sensitive data. In one instance, a GPT-3-based application was manipulated to reveal confidential prompts and internal data through carefully constructed user inputs.
LLM Pentesting Checklist
Large language model penetration testing is not random fuzzing or prompt-fu. It uses a structured checklist. You should start by mapping the full query path and tracking how a user query moves through the system:
query → embedding → retrieval → LLM output
Here, each layer has its own risks. A 2025 Knostic assessment showed that even top-tier systems like ChatGPT-4.5 expose weaknesses if the full stack is not scoped. For example, revealing its system prompt if asked in a specific way, making it much easier for attackers to bypass controls.
Next, red teamers must gather logs and metadata. Pull queries, vector-store details, and ACL configs. This helps you spot patterns and see where the system may have gaps.
Once you know the system, simulate attacks. This includes prompt fuzzing, where you flood the LLM with crafted inputs to see what slips through. Run RAG boundary tests to check how external documents affect answers. Try vector-poisoning attempts by injecting bad data into the embedding store.
Finally, document everything. Write up the findings. Make it easy for downstream teams to remediate leaks. Then, validate the fixes by re-running the tests.
Copilot & ChatGPT Pentesting
LLM platforms like Microsoft Copilot and OpenAI’s ChatGPT are integrated in many enterprise tools. But they come with their own pentesting needs.
Copilot audit-log hooks & common exploits
First, it is important to highlight that Copilot’s audit logs are needed for security checks. Pentesters must test if the logs capture all user interactions, including sensitive prompts or hidden malicious inputs. Knostic’s assessments show that missing hooks in audit logs allow undetected access attempts.
ChatGPT enterprise risks & test cases
As Knostic’s March 2025 blog points out, ChatGPT-4.5 will share its system prompt if asked in a specific way. This lets attackers understand internal instructions, jargon, and call structures. That knowledge can be used to craft jailbreaks or tool manipulation attacks. Pentesters should always include system-prompt tests when assessing any ChatGPT deployment.
Shared lessons for any search assistant
All enterprise search assistants share common risks. Whether it’sCopilot, ChatGPT, or a private LLM, attackers will target: prompt injection, oversharing connectors, weak ACL enforcement, missing redaction layers.
How Knostic Can Help with LLM Pentesting?
Knostic enhances LLM pentesting with nterprise-grade tools designed to systematically probe, map, and remediate AI-created vulnerabilities:
- Prompt Traceability: Map prompt-to-response flows across users, teams, and access layers, exposing inference-layer leakage that bypasses traditional DLP and RBAC
- Policy-Aware Exposure Scoring: Every response is evaluated against organizational sensitivity labels and access policies, enabling you to prioritize findings based on real-world business impact
- Automated Oversharing Detection: Detect when AI output reveals information from over-permissive or misclassified sources, even if the underlying file permissions appear valid
- Red Team-Ready Simulation Engine: Launch persona-based simulations to emulate internal and external actors, surfacing what different roles can extract through chained or indirect prompt strategies
- RAG Path Auditing: For hybrid setups, trace the full retrieval pipeline, from vector query to LLM output, to identify poisoned embeddings, hallucinated grounding, and supply chain weaknesses
- Remediation Workflow Integration: Trigger fixes via integrations with tools like ServiceNow or Microsoft Purview, including permission rollbacks, owner notifications, and linked audit trails
Together, these capabilities make Knostic an essential partner for red teams focused on securing LLM deployments in fast-moving threat environments.
What’s Next?
The future of LLM pentesting is moving fast. Attackers develop new techniques constantly, so security teams must keep pace with updated tools and playbooks.
Jailbreak playbooks, like this one from Knostic, outline common jailbreak methods and how to defend against them. They give teams practical strategies for securing their LLM deployments.
FAQ
- How is LLM pentesting different from regular app testing?
LLM pentesting focuses on the unique, dynamic risks of language models such as prompt injection and vector-store poisoning, while regular app testing targets static vulnerabilities like SQL injection or cross-site scripting.
- Does pentesting expose training data?
Not directly. Pentesting simulates attacks to reveal potential leaks, but it doesn’t access or expose the training data unless the model itself is vulnerable to such leakage.
- How often should we retest fine-tuned models?
Re-testing should occur after any significant update, fine-tuning, or integration with new data sources, ideally quarterly or with every major deployment cycle to prevent patch drift.
- What makes tools like Knostic different from vendor tools?
Independent tools can test systems without relying on the vendor’s own security layers.
Tags:
AI data security
