Top 5 LLM Security Risks Every Business Must Address

What it is: Prompt injection occurs when an attacker manipulates the input to an LLM to override its intended behavior. This can lead to unauthorized actions, data leakage, or misleading outputs.

Attacker’s angle: A malicious user crafts a cleverly worded prompt that hijacks the LLM’s behavior. They might embed hidden instructions, override safety filters, or trick the model into revealing internal data.

Business impact:

• Manipulated LLM prompts reveal internal documentation or bypass safety filters.
• Brand reputation suffers if the model defames competitors or outputs offensive content.
• Prompt injection leads to compliance violations and lawsuits in regulated industries.

What it is: LLMs trained or fine-tuned on sensitive datasets may inadvertently expose personally identifiable information (PII) or proprietary business data.

Attacker’s angle: By probing the LLM with targeted prompts, an attacker attempts to extract sensitive customer or employee data, including names, addresses, financial records, or medical history.

Business impact:

• Leaked customer data could trigger GDPR or CCPA violations, leading to hefty fines.
• Trust erosion spreads among users and partners.
• Potential class-action lawsuits follow exposed data leads to identity theft or fraud.

What it is: LLMs can generate outputs that are misleading, offensive, or harmful if not properly validated or filtered before use.

Attacker’s angle: An adversary exploits weak output validation by feeding the LLM prompts that produce harmful, misleading, or defamatory content—knowing it will be published or acted upon without review.

Business impact:

• Publishing AI-generated content without review risks misinformation, defamation, or inappropriate messaging.
• Outputs that cause harm or violate advertising standards lead to legal liability.
• Damage to brand credibility and customer trust.

What it is: LLMs may confidently generate false or misleading information, especially when prompted with ambiguous or adversarial inputs.

Attacker’s angle: By feeding ambiguous or adversarial prompts, attackers coax the LLM into confidently generating false information, which is especially dangerous in regulated industries such as health or finance.

Business impact:

• Disseminating inaccurate information misleads customers, partners, or investors.
• Misinformation leads to financial or reputational harm and the risk of lawsuits.
• Regulatory scrutiny follows in sectors like healthcare, finance, or legal services.

What it is: Attackers can exploit LLMs by submitting long or complex prompts that consume excessive compute resources, driving up costs or degrading performance.

Note that unbound consumption can be caused not only by malicious attackers but also by naïve users who are unaware of the costs behind LLM prompt usage.

Attacker’s angle: A botnet or malicious user floods your LLM endpoint with massive, complex prompts designed to consume excessive tokens and compute resources.

Business impact:

• Unexpected spikes in cloud usage and billing.
• Denial-of-service scenarios where legitimate users are blocked.
• Financial strain and operational disruption.

• Deploy AI-specific security solutions that monitor prompt behavior, detect anomalies, and enforce usage policies.
• Implement output validation layers to catch harmful or misleading content before it reaches users.

• Use differential privacy and data masking to prevent sensitive information leakage.
• Rate-limit and throttle requests to prevent overconsumption and abuse.
• Train staff and developers on secure prompt engineering and adversarial testing.