Abhay Bhargav
April 25, 2024

The Top 7 Security Issues in Large Language Models

Are we giving too much power to the machines? Large Language Models (LLMs) like GPT and BERT are the titans of the AI world. They’re changing the way we use technology. Creating emails, generating codes—these AI tools have become our assistants across industries. But it comes with a price and, well, headaches.

As we rely more on LLMs, so do the stakes that come with them. A single vulnerability, once exploited, can create unimaginable consequences. Listen: 90% of cybersecurity professionals are concerned about AI and machine learning threats, yet only 31% of IT professionals felt their organizations were highly prepared for AI-powered cyberattacks. The gap is alarming, isn’t it?

That being said, I think it’s high time for us to discuss LLM security. This blog is about the top 7 issues that are keeping the pros up at night.

Table of Contents

  1. LLMs can leak sensitive data by accident.
  2. Model poisoning during the training process.
  3. Output manipulation spreads fake news.
  4. Intellectual property risks because of ingested data.
  5. LLM regulations and ethical standards vary across different countries.
  6. The ethical landscape should be shaping LLM development.
  7. Let’s talk about mitigation strategies!
  8. Stay curious, collaborative, and committed to security.

LLMs can leak sensitive data by accident.

Large Language Models require vast data sets to train on. With this data-hungry nature, there’s the danger of the inadvertent leakage of sensitive information. Discretion is important, yet despite being designed to generate helpful responses, it can occasionally let slip bits of confidential data it was trained on. When an LLM reveals pieces of its training data, it might not recognize the difference between public information and someone's private details.

The consequences?

  • Users' private details, such as addresses, phone numbers, or even health records, could be unintentionally revealed.
  • Confidential financial information, including credit card details, bank account numbers, or proprietary financial forecasts, could be accidentally disclosed. This is a risk for both individuals and businesses.
  • Trade secrets, unreleased product information, or strategic plans might find their way into the wrong hands. So no more competitive advantages, I guess.
  • Organizations might find themselves in breach of data protection regulations like GDPR. Can you imagine the fines and legal repercussions? How about the loss of consumer trust?
  • Sensitive information related to security protocols or system architectures could be leaked. Welcome attackers, you can come in.

This is a full-blown crisis waiting to happen, and the first line of defense is acknowledging the problem. Let’s talk more later about how to address these security issues.

Model poisoning during the training process.

Model poisoning is no joke, as it is designed to corrupt the very heart of Large Language Models (LLMs)—their training process. Malicious data will be injected, or the training algorithm will be tweaked to sabotage the model’s integrity.

These kinds of attacks exploit the model's learning process, subtly manipulating it to produce outputs that serve the attacker's purposes rather than genuine, reliable responses. The methodology of these can be complex, like using techniques like Gumbel-softmax trick to manipulate token probabilities and induce specific, undesired behaviors in the model under attack.

The consequences?

  • The model's output quality can significantly deteriorate. Expect nonsensical or irrelevant responses.
  • Model poisoning can force the LLM to generate content that deviates from the aligned goals set by its developers. There might be harmful or offensive material.
  • There's a risk of the model divulging sensitive or private information. This is a direct consequence of the corrupted training data or objectives.
  • Attackers might engineer the model to generate outputs that, when utilized, expose vulnerabilities in systems or software.
  • The model could be weaponized to generate content that supports phishing campaigns, spreads misinformation, or even instigates discord.

Output manipulation spreads fake news.

These advanced models are integral to generative AIs. They have the capability to produce content that spans the spectrum from incredibly insightful to potentially biased, false, or even harmful. The underlying issue usually has something to do with the data they have been fed or how the prompts were engineered.

If you’re thinking that this is only about the occasional factual inaccuracy or a nonsensical reply, then you’re wrong. We’re talking about the propagation of biased viewpoints, the spread of misinformation, or the inadvertent disclosure of sensitive information that really raises red flags. Here’s an example: an LLM model generating misleading medical advice. Can you imagine how harmful that is?

The real-world implications?

  • Determining whether the bias originates from the training data, model architecture, or the fine-tuning process. Tedious!
  • Adjusting models to eliminate bias or false outputs without diminishing their versatility or overall performance.
  • While input and output controls can prevent some forms of biased or harmful content, making sure these filters are effective without overly restricting the model's functionality is difficult. 
  • LLMs can generate outputs that are factually incorrect or misleading, known as 'hallucinations', which are challenging to predict and prevent due to their complex nature. 
  • As societal norms and definitions of fairness evolve, continuously updating models to reflect these changes is an ongoing challenge.
  • Making sure that LLMs are not used for unethical purposes, such as spreading misinformation or automating deceptive practices, requires constant vigilance and innovative solutions. 

Intellectual property risks because of ingested data.

You already know that LLMs need huge amounts of data to learn and generate new outputs, but did you know that they also include copyrighted content? There were some concerns about copyright infringements, especially when these models produce content that closely mirrors the material they were trained on.

The considerations?

  • LLMs use huge data sets to learn and generate new content. There could be copyrighted materials in these datasets that could lead to outputs that closely resemble the original copyrighted works.
  • Are AI-generated works liable for copyright laws? If so, who holds that copyright—the creator of the AI, the user who prompted for the output, or the AI itself?
  • Is AI-generated content a new creation, an imitation, or simply an iteration of existing copyrighted materials?
  • Organizations that use LLMs face the risk of unintentionally infringing on existing copyrights. Careful consideration and mitigation strategies will be needed.

Scalability vs. Security

We’ve been impressed. We felt threatened. As you read this, LLMs only continue to grow in capability. But along with that are the complexities of how to secure them against threats. Scaling LLMs involves improving their data processing capabilities, which inevitably need more data. While this is important for improving performance, this process also introduces significant security issues.

The challenges?

  • Adversarial attacks exploit the model's architecture and training process to manipulate outputs while undermining content moderation and spreading misinformation.
  • Model evasion techniques that spread misinformation and reduce the reliability of LLMs.
  • Impersonation is where attackers impersonate legitimate users, bypassing authentication controls to access LLM functionality for malicious goals such as disinformation and financial fraud.

LLMs advance with technologies like reinforcement learning and feedback-based learning. Continuously ingest data to improve their knowledge and capabilities. The auto-learning feature is helpful when improving responses over time, but as impressive as it can be, it presents a huge risk when it comes to privacy and data security. Without strong data governance controls, sensitive business or personal information can be easily accessed and misused by malicious actors.

LLM regulations and ethical standards vary across different countries.

Each region has its own set of rules that can influence how LLMs are developed, deployed, and managed, which can lead to a complex mosaic of compliance requirements.


As an example, Europe spearheads AI regulations by proposing the EU AI Act, which sets strict standards to make sure that AI systems are safe, transparent, and nondiscriminatory. This legislation categorizes AI practices by risk levels and urges heavier restrictions on high-risk applications, such as those that involve law enforcement or are susceptible to biases.

Algorithmic Accountability Act and the AI Disclosure Act in the US

The United States has taken a more decentralized approach. There are different legislative proposals, like the Algorithmic Accountability Act and the AI Disclosure Act. These proposed laws aim to take on transparency, fairness, and data privacy but have yet to be finalized.

Interim Administrative Measures for the Management of Generative AI Services by China

China also has established principles and new regulations, such as the Interim Administrative Measures for the Management of Generative AI Services, which demonstrate the country’s proactive stance in AI governance.

Because of this global disparity in regulations, it’s more important than ever to develop universally accepted ethical guidelines for AI. Ethical considerations, central to AI governance, include addressing algorithmic biases, ensuring transparency, and maintaining privacy and accountability.

The ethical landscape should be shaping LLM development.

Bias. Fairness. Societal impact. These three are at the top of the list when there are discussions about the ethical considerations that LLMs raise. They are not immune to social biases that are embedded in their training datasets. Because of this, their output can potentially reinforce stereotypes and cultivate unfairness, which makes the implementation of robust bias mitigation strategies the focus for developers.

The long term impact?

  • Persistent biases and unresolved security vulnerabilities can erode public trust in AI technologies. If you don’t trust AI to perform fairly or securely, will you be using it?
  • AI can make already existing societal inequalities worse. For example, biases in AI can be the reason for unfair treatment across different social groups that will impact job opportunities, legal outcomes, and access to services.
  • Making sure that LLMs are both effective and secure requires sturdy regulatory frameworks. The problem is that when creating and enforcing these regulations, international cooperation is needed. Without it, there might be a ‘race to the bottom’ where countries or companies exploit lenient standards.

Let’s talk about mitigation strategies!

To effectively take control of security in Large Language Models (LLMs), current strategies involve a sophisticated approach that includes ongoing research, community engagement, and proactive policy-making. Here’s how these elements come together to enhance the security landscape for LLMs:

Ongoing research to improve the security capabilities of LLMs.

We should focus on developing new techniques that will detect and respond to threats, improving encryption, and strengthening models against security attacks. As an example, researchers are continuously exploring ways to make algorithms more resistant to manipulation by introducing adversarial training methods to expose models to a wide range of attack scenarios during the development phase.

Community engagement with cybersecurity communities to stay ahead of threats.

When you share knowledge, tools, and strategies, the community can collectively respond to new vulnerabilities. Open-source projects and collaborative platforms help to rapidly spread security advancements and prompt community-driven solutions to newly identified problems. Having this much collective effort will refine existing security measures and cultivate an environment where information is shared openly and promptly.

Policymaking to set standards and regulations.

This part is important when setting standards and regulations that govern the development and deployment of LLMs. Policies need to address the dual aspects of promoting innovation while guaranteeing privacy, fairness, and security. For example, frameworks like the EU’s General Data Protection Regulation (GDPR) set benchmarks for data privacy that directly impact how LLMs are trained and used to make sure that personal data is handled responsibly.

Policymaking also includes updating existing ones to keep pace with technological advancements. As LLM technologies become more effective, the policies that keep them in place should too. We’re talking about specific provisions for AI transparency, accountability, and the interpretation of AI decisions. These are important when building trust and understanding among AI users.

Stay curious, collaborative, and committed to security.

Large Language models are all the rage right now. They streamline processes, aggregate data like no other, and more. They’re changing the way we work with technology. But are they safe? 

When it comes to the security of LLMs, we need to innovate and, at the same time, commit to security, ethical considerations, and regulatory compliance. Today, more than ever, is when we need the efforts of the cybersecurity community, proactive policy-making, and continuous research.