
Integrating legacy apps & plugins that reinforce a website’s consumer & marketplace functionality have been common practice web dev for several decades. AI machine learning applications were first incorporated into finance credit scoring algorithms in the 1980’s & are still utilized by banks today to formulate credit scores (FICO) & detect fraud to protect consumers.
The genesis of cybersecurity took shape in the 1970’s after the first self-replicating computer worm, “Creeper,” was created by the ARPANET research project. To counter the replicating computer worm, Ray Tomlinson created the 1st antivirus software, “Reaper,” eliminating the invasive worm. By the 90’s AI virus heuristic detection & firewall network protection technologies were widely adopted with the advent of the Internet. Cybersecurity automations have evolved alongside the development of a more sophisticated Internet & as new threats emerge.
Today, we have the revolution of Large Language Models (LLMs), such as Meta LLaMA & Google PaLM 2, entering the digital space. LLMs utilize natural language processing (NLPs) to complete generative tasks, such as communicating with consumers through text & speech prompts, & assisting with all customer service orientated functions from the first impression to checkout. LLMs gather an unimaginable amount of data from a multitude of Internet sources & perform deep learning patterns to derive answers to consumer queries.
AI LLMs Introduce New Cybersecurity Threats

Anytime a new app, API, or plugin is connected to a website framework, the newly installed software poses several unknown risks to the existing site, such as, triggering fatal errors & the derivation of several cybersecurity vulnerabilities. Akin to legacy third-party applications, Large Language Models (LLMs) introduce new cybersecurity threats; both known & unknown.
Consumers have voiced several valid concerns about AI LLMs & their unwitting admission to new levels of malicious hacking threats. Understanding & awareness of the threats LLMs interfaces introduce to a website’s framework are fundamental for cybersecurity professionals & website programmers to protect their consumers’ biometric & private data.
Β Β Known LLM Threats
Prompt Injection hacking attacks reformat the speech language output of a programmed AI LLM interface. Prompt injections modify customer interaction tools, such as chatbots, into malicious hacker languages that compromise a user’s computer.
Remote Access of a compromised LLM interface is a critical security implication that derives from hacking the source code of a LLM plug-in, or application. Remote-access attacks allow hackers complete access of an unsuspecting webhost’s infrastructure, disrupting customer interactions & their personal data privacy & security.
Sensitive Information Disclosure is a potential security risk when Large Language Models, such as OpenAI ChatGPT, are integrated within high-stakes application domains that include healthcare, financial, & counseling websites. When a high-stake domain user interacts with a LLM conversational agent, the LLM may inadvertently disclose consumers’ sensitive data, such as, medical records & paystub history.
Insecure Output Handling is a security risk that allows unintended privileges to a hacker via the LLM interface. Insecure output handling exploitation is utilized by a LLM that processes unsecured data downstream to an end user through prompts. From the compromised LLM interface, the hacker can remote upload dangerous shell commands & malicious browser attacks, such as cross-site scripting (XSS).
Training Data Poisoning hackers exploit the open access of the training data of Large Language Models. Unsecured training modules of the LLMs’ framework may be corrupted to discover new vulnerabilities & provide hackers backdoor access to unsuspecting domains.
Denial of Service (DoS) attacks occur when LLMs’ server resources are overloaded due to the stress of malicious incoming traffic. Typically, the DoS attack is performed by a flood of bots & not real users. During a Denial of Service attack, the server’s resources are unavailable to real users & can create a total loss of domain function to consumers.
Actionable Cybersecurity Measures for AI LLMs

Actionable cybersecurity measures for AI LLMs births a new array of proactive security techniques. New Large Language Model cyberthreats are discovered daily. Machine learning LLM threat heuristics & a worldwide collaboration of security experts is essential to perfect the end users experience & safety validations of the third-party, opensource software applications that Artificial Intelligence LLMs provide.
Integrating AI LLMs to enhance the consumer experience is an excellent tool available to all web developers. It is our duty to provide our consumers a safe & best practices digital space for all their LLM interactions on our domains.
LLMs Prompt Injection Attack Prevention
Prompt Injection Attacks are prevented by combing through the complete code structure of all inbound prompt interactions that the LLM produces. Implementing machine learning algorithms to detect & deter malicious patterns & strings of code prompts.
Block LLM Remote Access
LLM application remote access can be blocked by implementing remote browser isolation (RBI) directives utilizing cloud-native firewall platforms, such as Cloudflare “zero trust,” that adds a controllable layer of protection to browsers for networks.
Mitigate LLM Sensitive Information Disclosure Risks
It is imperative to finetune LLMs prompt customizations & retrieval-augmented generation (RAG) systems to ensure a sanitized & streamlined application experience for consumers that is industry specific. Inadvertent interface prompts may trigger exposure of a user’s personal data to third party deep learning resources during retrieval of results. Restricting the parameters of LLM training models language mitigates potential data leaks & privacy disclosure risks.
Restrict Insecure Output Handling
OWASP Application Security Verification Standard (ASVS) validation guidelines are available for web designers assimilating LLMs technologies within their domain frameworks. The ASVS insecure output handling prevention guide illustrates methods to validate response handling of the LLM backend functions. Similar to W3C markup validation service for HTML web developers, ASVS provides input & output LLM language validation standards for sanitization & encoding.
LLMs Training Data Anti-Poisoning Measures
Staying up-to-date on the continuously evolving abnormalities LLM training data may pose is a key factor in combating potential training data poisoning. Removing anomalies that are not within scope of industry provides an anti-poisoning measure that limits cyberthreats. Verification & perpetual testing against diverse input scenarios of LLM training data ensures a secure & reliable experience for consumers.
LLM Denial of Service (DoS) Mitigation
Monitoring LLM system usage will help identify a LLM DoS attack. Strict validation & sanitization of LLM input prompts assures that file size limitations & formats of input prompts are within a set parameter & do not deviate allocated resources.
