Large language Model (LLM) prompt injections represent one of the most significant security challenges facing AI today. In LLM prompt injection attacks, malicious actors manipulate the behaviour of the targeted AI model through adversarial or hidden instructions to access sensitive information and exploit organisations.
Prompt injection attacks are bad news- they compromise data, disrupt operations, and significantly impact your organisation’s reputation. With AI usage exploding across the world, securing your LLMs is no longer optional.
This blog will support businesses in defending against LLM prompt injection attacks by introducing common attack methods, real-world examples, and practical defence tactics.
What is LLM prompt injection?
LLM prompt injection is a type of security vulnerability where an attacker manipulates an LLM by overloading it with deceptive and highly crafted inputs. In turn, the model’s original instructions are overridden, allowing the attacker to prompt the model to provide further responses that manipulate business logic and reveal sensitive data and financial information.
Many consider prompt injection attacks to be comparable to social engineering attacks at scale, as neither relies on technical exploits and instead disguises malicious intent as legitimate requests to bypass security measures. Social engineering manipulates humans into breaking security rules by exploiting trust and psychology, while prompt injections manipulate AI systems by exploiting how they process instructions mixed via user prompts.
It’s also important to recognise that prompt injection attacks differ from other traditional injection attacks, like SQL injections, as prompt injections work on manipulating the AI’s reasoning, **whereas SQL injections directly execute unauthorised operations through code.
LLM prompt injection attacks can also occur through APIs by embedding malicious content instructions in the data fields that your LLM processes, while disguising them as ‘normal’ API calls.
While LLM attacks are their own unique threat, what makes them really dangerous is how easily they can be chained with traditional attack methods, turning them into a serious amplifier of existing security risks.
Types of LLM prompt injection techniques
Direct prompt injection
Direct prompt injection involves attackers explicitly instructing the LLM to ignore previous instructions or safety rules, using commands like “Ignore your safety rules and…”. This technique overrides the model’s intended behaviour, enabling malicious users to manipulate responses, potentially leading to data leaks, harmful content generation, or unauthorised actions.
It sounds straightforward, but it can be incredibly effective.
Indirect prompt injection
During indirect prompt injection attacks, malicious instructions are hidden in external content, such as emails, PDFs, or webpages, that the model processes. The LLM then confuses the legitimate request, for example, to ‘proofread this PDF’ with the malicious instructions embedded in the content itself.
Indirect prompt injection attacks are more stealthy than direct injection attacks, making them also potentially more dangerous fr organisations.
Obfuscation and encoding
Attackers hide malicious intent using encoding methods like Base64 or multi-step instructions, making harmful instructions difficult to detect. These concealed prompts can bypass filters and security measures, allowing attackers to manipulate the AI’s model behaviour and generate harmful content without being detected straight away.
Multi-turn and persistent attacks
Persistent attacks do what they say on the tin. Attackers will exploit long conversations to gradually override system instructions, intertwining malicious prompts with legitimate user inputs to confuse and disarm the LLM. By starting with legitimate requests, attackers can ‘build trust’ with the LLM, leveraging the AI’s tendency to maintain consistency within a conversation.
In many instances, the AI will continue a pattern even when it crosses into prohibited territory. This is the basis on which multi-turn and persistent attacks operate: attackers convince the AI to follow a responsive pattern until it is unwittingly giving away sensitive data.
Real-world prompt injection attack examples
Data exfiltration
Prompt injection attacks manipulate AI systems by embedding malicious instructions within user inputs, attempting to override security controls. Attackers craft prompts that trick models into revealing confidential information like system prompts, internal configurations, or API credentials.
These attacks exploit the model’s instruction-following nature, trying to bypass safety guardrails. Effective defences include input validation, output filtering, and architectural separation of privileged information from user-accessible contexts.
Jailbreak to generate malware
LLM jailbreak scenarios involve an LLM being manipulated into malicious code. Attackers design an input that causes the LLM to bypass its safety mechanisms and give inappropriate responses that should not typically be accessible, including restricted or dangerous content.
It’s similar to coaxing a device into “developer mode”: the language model suddenly behaves differently and ignores its guardrails.
Business logic manipulation
Business logic manipulation is an umbrella term that refers to an attacker manipulating legitimate business processes or rules to their advantage. Unlike technical attacks, business logic vulnerabilities don’t exploit technical flaws. Instead, they exploit the typical functionality of the application or system.
Workflows such as automated financial approvals, AI assistants, or customer support chatbots are particularly vulnerable to LLM business logic manipulation, and can potentially lead to financial losses and impact customer trust.
The risks and implications of prompt injection
The risks and potential implications of prompt injection attacks can span from reputational damage to customer harm and compliance issues, depending on the severity of the injection attempts themselves.
Prompt injection attacks present significant risks that organisations should carefully consider. These vulnerabilities may result in reputational challenges if AI systems generate harmful or inappropriate content, potentially affecting customer trust and brand perception. For companies operating in regulated sectors, prompt injections can introduce compliance complexities, with possible implications for data protection laws, financial regulations, or industry-specific standards.
Ensuring customer safety remains a priority. Malicious prompts cause AI systems to offer unsafe advice, unintentionally disclose sensitive information, or enable fraudulent activities.
In competitive industries, these weaknesses could be exploited for corporate espionage or to disrupt competitor operations.
How to detect and defend against prompt injection
While the risks of prompt injection vulnerabilities are significant, there are accessible methods to detect and defend against them. Here are some best practices your organisation can implement to protect itself against malicious system prompts.
Prompt injection detection
Prompt injection detection uses multiple defensive layers to protect your LLM from untrusted user input and malicious prompts. For example, anomaly detection ****can identify unusual input patterns or token sequences that do not align with your normal business processes.
Input sanitisation can filter malicious instructions, special characters, and suspicious formatting before processing, effectively flagging stealthy indirect prompt injections. Additionally, adversarial token monitoring tracks known attack patterns and delimiter abuse that could surpass more obvious prompt injection detection strategies.
Don’t worry, there are plenty of prompt injection detection tools to support you in building a robust defence strategy. Tools like Lakera Guard, Rebuff, and Prompt Armour provide real-time detection, while platforms like Microsoft Azure AI Content Safety and LLM Guard offer comprehensive filtering solutions to prevent manipulation of AI system prompts.
Guardrails and filtering
Guardrails are an excellent way to moderate content and safeguard your organisation from prompt injection attacks. They compromise of policies and protective strategies that enforce security in generative AI becoming increasingly relevant as businesses adopt AI tools and strategies in their workflows.
A regex filter is a simple yet powerful type of guardrail that uses regular expressions (regex) to identify and act on specific text patterns. While guardrails can involve more complex methods like using an LLM to interpret context, regex filters are rule-based, making them a great addition to any existing AI defensive security strategy.
Additionally, allow/deny lists define what content is permissible or forbidden within your LLM. Deny lists are threat-centric models which specifically detail what content, phrases, and sequences are banned, authorising anything else. On the other hand, allow lists function specifically for listed ‘authorised’ content, phrases and sequences, meaning all deviations from these are prohibited. Both present strengths and weaknesses, and further research into this guardrail method could help determine which is best for your LLM.
LLM pentesting
An efficient method to evaluate the security of your LLM is to invest in LLM penetration testing. LLM penetration testing provides a thorough and documentable method of proactively uncovering injection vulnerabilities, empowering your organisation to implement LLMs without the risk of potential data leaks or operational disruptions.
Emerging pentesting frameworks for LLM applications and AI-specific pentesting modules provide structured methodologies for assessing prompt injection risks and adversarial behaviour.
Typical LLM pentest methodologies blend manual probing and automated scripts to uncover vulnerabilities within your LLM, providing actionable insights.
Penetration testing is complementary to LLM red teaming, providing structured and repeatable security insights. LLM red teaming simulates real-world attack scenarios to test how your model responds to malicious inputs. LLM red teaming helps organisations understand potential weaknesses, strengthen model defences, and build safer, more resilient AI systems through continuous, evidence-based improvement.
Context isolation
In context isolation, sandboxing and retrieval-augmented generation (RAG) separate external inputs from trusted instructions. This isolation helps prevent malicious input from influencing the model’s behaviour, reducing the attack surface and mitigating prompt injection attacks. By clearly distinguishing user-generated content from system prompts, you can better safeguard sensitive operations and ensure your AI is resilient against malicious tampering.
Stay ahead of emerging threats surrounding AI with OnSecurity’s LLM red teaming services, designed to provide you with real-time insights into your security posture through our consultative, platform-based approach.
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