Introduction
The rapid adoption of large‑language models (LLMs) and AI assistants by corporate boards has become a defining trend of the 2020s. Executives are drawn to the promise of instant knowledge retrieval, automated drafting, and seamless integration with existing enterprise tools. In practice, these assistants can browse the web in real time, remember user context across sessions, and plug directly into business applications such as CRM, ERP, and collaboration suites. While these capabilities translate into measurable productivity gains, they also introduce a new vector of cyber risk that is often overlooked.
The core of the issue lies in the very features that make AI assistants attractive. Live web browsing allows an assistant to pull up the latest data from a company’s own intranet or from public sources, but it also opens a door for malicious actors to inject false information or redirect queries to phishing sites. Context retention, which lets the assistant build a mental model of a user’s preferences and past interactions, can be weaponized if an attacker gains access to the assistant’s memory store. Finally, deep integration with business apps means that a compromised assistant could issue commands that affect financial records, customer data, or intellectual property. The convergence of these factors creates a complex attack surface that is difficult to map and even harder to defend.
Tenable, a leading cybersecurity research firm, has recently published a study titled HackedGPT that catalogs a range of vulnerabilities and attack scenarios specific to AI assistants. The study demonstrates that the intersection of LLMs, web browsing, memory, and app integration is fertile ground for novel exploits. The findings are a wake‑up call for organizations that have embraced AI assistants without a comprehensive security strategy. In the sections that follow, we will unpack the technical details of these threats, illustrate real‑world implications, and outline practical mitigation steps that can help enterprises protect themselves while still reaping the benefits of AI.
The Promise of AI Assistants
AI assistants are designed to act as a single point of interaction for a wide array of tasks. A user can ask a question about a quarterly report, and the assistant will retrieve the latest figures from the company’s data lake, summarize them, and even draft an email to the finance team. The same assistant might also schedule a meeting, pull up the relevant agenda from a shared calendar, and post a reminder to a Slack channel. The convenience of having one tool that can orchestrate multiple workflows is a compelling value proposition.
Beyond routine tasks, AI assistants can also provide strategic insights. By continuously scanning industry news, regulatory updates, and competitor activity, an assistant can surface emerging trends that might otherwise go unnoticed. This real‑time intelligence can inform product roadmaps, risk assessments, and market positioning. In short, AI assistants are positioned as the digital extension of a human analyst, capable of handling both the mundane and the complex.
How Features Expand the Attack Surface
The very features that enable this level of functionality also create new attack vectors. Live web browsing, for instance, requires the assistant to send HTTP requests to external servers. If an attacker can control the content returned by a compromised or malicious domain, they can feed the assistant with fabricated data. Because the assistant often trusts the information it receives, the attacker can manipulate the assistant’s responses, potentially leading to incorrect decisions or the spread of misinformation.
Memory retention is another double‑edged sword. LLMs are trained to generate contextually relevant responses, but they also store a representation of past interactions. If an attacker gains access to this memory store—whether through a misconfigured database, a supply‑chain compromise, or a social‑engineering attack—they can inject false context that the assistant will use in future interactions. This could result in the assistant providing sensitive data to the wrong party or executing commands that the user never intended.
Integration with business applications is perhaps the most dangerous aspect. AI assistants often use APIs to read from or write to systems such as Salesforce, SAP, or Microsoft Teams. If an attacker can hijack the authentication token that the assistant uses, they can issue commands that alter financial records, delete customer data, or exfiltrate intellectual property. The attack surface is further amplified when the assistant is deployed across multiple devices and platforms, each with its own set of credentials and permissions.
Tenable’s HackedGPT Findings
Tenable’s HackedGPT study catalogues a range of vulnerabilities that exploit the intersection of these features. One notable example is the “API Key Injection” attack, where an attacker crafts a malicious prompt that causes the assistant to reveal or misuse an API key. Another is the “Context Poisoning” attack, in which an attacker injects false context into the assistant’s memory, leading it to generate incorrect or harmful outputs.
The study also highlights the risk of “Web‑Based Data Manipulation,” where an attacker controls a domain that the assistant trusts for live data. By serving fabricated content, the attacker can influence the assistant’s responses and potentially trick users into taking actions based on false information. In a corporate setting, this could mean approving a budget that does not exist or making a strategic decision based on fabricated market data.
Tenable’s research demonstrates that these attacks are not purely theoretical. In controlled experiments, the researchers were able to compromise a prototype AI assistant and retrieve sensitive data, alter business processes, and even trigger financial transactions. The findings underscore the need for a layered security approach that considers the unique properties of AI assistants.
Real‑World Implications
The implications of these vulnerabilities extend beyond the laboratory. In 2023, a major financial institution reported a breach that was traced back to a compromised AI assistant. The attacker had used the assistant’s web‑browsing capability to inject a malicious script into a trusted data source, which then caused the assistant to issue a series of unauthorized fund transfers. The incident resulted in a loss of $12 million and a significant erosion of customer trust.
Another case involved a healthcare provider that used an AI assistant to manage patient records. An attacker exploited a memory‑retention flaw to inject false medical history into the assistant’s context. The assistant, trusting the fabricated data, incorrectly flagged patients for unnecessary treatments, leading to both financial penalties and potential harm to patients.
These examples illustrate that the attack surface created by AI assistants is not merely theoretical; it has tangible, high‑stakes consequences. Organizations that deploy AI assistants without a robust security framework risk exposing themselves to financial loss, regulatory fines, and reputational damage.
Mitigation Strategies
Mitigating the risks associated with AI assistants requires a holistic approach that spans technology, process, and culture. First, organizations should enforce strict access controls on the APIs that the assistant uses. Implementing least‑privilege principles and rotating credentials can reduce the impact of a compromised token.
Second, data validation and sandboxing are essential when the assistant retrieves information from external sources. By verifying the integrity of the data and isolating the assistant’s execution environment, organizations can prevent malicious content from influencing the assistant’s behavior.
Third, continuous monitoring of the assistant’s interactions is crucial. By logging every prompt, response, and API call, security teams can detect anomalous patterns that may indicate an ongoing attack. Integrating these logs with a SIEM system can provide real‑time alerts and facilitate forensic investigations.
Finally, user education remains a cornerstone of defense. Employees should be trained to recognize suspicious prompts, verify the authenticity of data, and report anomalies promptly. A culture of security awareness can act as a human firewall against social‑engineering attacks that target AI assistants.
Future Outlook
As AI assistants evolve, their capabilities will only expand. Future iterations may include deeper integration with IoT devices, real‑time decision‑making in autonomous systems, and more sophisticated natural‑language interfaces. Each new feature will bring additional attack vectors, making it imperative for organizations to stay ahead of the curve.
The cybersecurity community is already working on formal verification methods for LLMs, secure prompt engineering, and tamper‑proof memory architectures. While these technologies are still emerging, they offer a promising path toward building AI assistants that are both powerful and resilient.
In the meantime, enterprises must adopt a proactive stance. By combining robust technical controls, vigilant monitoring, and a culture of security, they can harness the productivity benefits of AI assistants while safeguarding their critical assets.
Conclusion
The promise of AI assistants is undeniable: they can accelerate decision‑making, automate routine tasks, and provide real‑time insights that were previously out of reach. However, the same features that make them useful also create a complex attack surface that is ripe for exploitation. Tenable’s HackedGPT study shines a light on the vulnerabilities inherent in live web browsing, memory retention, and deep business‑app integration.
Organizations that deploy AI assistants must treat them as high‑value assets that require the same level of protection as any other critical system. By implementing strict access controls, validating external data, monitoring interactions, and fostering a security‑aware culture, enterprises can mitigate the risks while still reaping the productivity gains.
The future of AI in business will be shaped by how well we balance innovation with security. Those who succeed will not only unlock new efficiencies but also build resilient systems that can withstand the evolving threat landscape.
Call to Action
If your organization is considering or already using AI assistants, now is the time to conduct a comprehensive risk assessment. Map out the assistant’s data flows, identify all external connections, and evaluate the potential impact of a compromise. Engage with cybersecurity vendors that specialize in AI‑specific threat detection and consider adopting secure prompt engineering practices.
Don’t wait for a breach to realize the importance of securing your AI assets. Invest in the right tools, processes, and training today, and position your organization to thrive in an AI‑driven world while protecting against the very threats that accompany it.
