Enterprise AI Analysis: The Dangers of Using Public LLMs for Security Investigations

A critical review of the research paper "Using LLMs for Security Advisory Investigations: How Far Are We?" and its profound implications for enterprise cybersecurity. We break down the findings and outline a strategic path forward with custom, verifiable AI solutions from OwnYourAI.com.

Executive Summary: A Critical Wake-Up Call for Enterprises

The rapid adoption of Large Language Models (LLMs) like ChatGPT in enterprise workflows presents both tantalizing opportunities and severe, often hidden, risks. This is especially true in cybersecurity, where accuracy and reliability are non-negotiable. A recent academic paper meticulously investigates this issue, providing hard data on the performance of a public LLM in handling security advisory tasks.

The study's findings are a stark warning: while public LLMs are adept at generating plausible-sounding content, they are dangerously unreliable for validating, describing, and managing real-world software vulnerabilities (CVEs). The research demonstrates that these models can confidently fabricate details for both real and non-existent vulnerabilities, fail to distinguish fact from fiction, and lack internal consistency. For any enterprise security team (SecOps), relying on these tools for critical investigations is equivalent to navigating a minefield blindfolded.

This analysis from OwnYourAI.com goes beyond the academic findings to translate these risks into tangible business consequences. We'll explore how these LLM failures can lead to wasted resources, incorrect mitigation strategies, and a dangerously false sense of security. More importantly, we'll outline the strategic alternative: building custom, verifiable AI solutions that integrate with authoritative data sources to deliver trustworthy, actionable security intelligence.

Research Deep Dive: Deconstructing the LLM's Failures

The research systematically tested an LLM's capabilities across three core security investigation tasks. The results reveal a consistent pattern of high plausibility and low factual accuracy. Let's examine each finding and its direct impact on enterprise security operations.

Finding 1: The Illusion of Plausibility (RQ1)

The first question the researchers posed was whether an LLM could generate trustworthy security advisories from a known CVE-ID. The model was tested with 100 real and 100 fake CVEs.

The results are alarming. The LLM generated plausible-sounding advisories for 96% of real CVEs and 97% of fake CVEs. This demonstrates a core competency in generating convincing text, but a complete inability to ground that text in reality. But how accurate were the "reliable" advisories for the real CVEs?

Enterprise Impact: Confidently Wrong is Worse Than Being Unsure

• Resource Drain: A security analyst, believing a generated advisory, could spend hours or days implementing the wrong patch, changing incorrect configurations, or searching for a non-existent attack vector.
• Increased Risk Exposure: While the team is chasing a hallucinated problem, the real vulnerability remains unpatched and exploitable.
• Erosion of Trust: When AI tools consistently provide incorrect information, teams will either stop using them entirely or, worse, become desensitized to alerts, potentially ignoring a real one.

Finding 2: Total Inability to Detect Deception (RQ2)

The researchers then tested if the LLM could identify the 100 fake CVE-IDs as invalid. The result was a categorical failure.

Enterprise Impact: Opening the Door to Social Engineering

• Weaponized Misinformation: Malicious actors could invent a CVE-ID and use it in a phishing email. An unsuspecting developer "validating" it with a public LLM would receive a convincing, false confirmation, potentially leading them to click a malicious link or install a compromised package.
• Operational Chaos: The spread of fake vulnerabilities can create "ghost alerts," sending security teams on wild goose chases and distracting them from legitimate threats, effectively creating a denial-of-service attack on the security team's attention.

Finding 3: The Crisis of Consistency and Accuracy (RQ3)

In the final test, the researchers explored if the LLM could correctly identify a CVE-ID from a given advisory description. This tests the model's ability to perform reverse lookups and its own internal consistency.

Enterprise Impact: Unsuitability for Automated Workflows

• Unreliable Automation: The 99% inconsistency rate (failing to identify the CVE from its own generated text) proves that these models cannot be used in a closed loop. Any automated workflow built on this foundation is guaranteed to fail.
• False Positives and Confusion: The fact that the LLM assigned a real, existing CVE-ID to a fake advisory 10% of the time is incredibly dangerous. It pollutes vulnerability databases with incorrect associations and sends analysts down rabbit holes, trying to connect a real vulnerability to a system it doesn't affect.

The Enterprise Path Forward: The OwnYourAI.com Strategy

The research isn't an indictment of AI in cybersecurity; it's an indictment of using the wrong tool for the job. Public, general-purpose LLMs are not security databases. True enterprise value comes from custom-built, specialized AI systems grounded in verifiable facts.

Interactive ROI Analysis: The Cost of Inaction vs. Custom AI

Relying on unreliable tools leads to wasted time and increased risk. A custom AI solution from OwnYourAI.com provides a clear return on investment by eliminating guesswork, accelerating investigations, and preventing costly mistakes. Use our calculator to estimate your potential savings.

Test Your Knowledge: The Risks of Unverified AI

The findings of this paper are critical for any professional in the tech industry. Take this short quiz to see how well you've grasped the key risks of using off-the-shelf LLMs for security.

Conclusion: It's Time to Own Your AI Strategy

The research paper "Using LLMs for Security Advisory Investigations: How Far Are We?" provides a definitive answer: we are not far enough to trust public, off-the-shelf LLMs with critical security functions. Their propensity for confident hallucination, inability to detect fakes, and lack of consistency make them a liability, not an asset, for any serious security operation.

However, this does not mean abandoning AI. It means getting smart about it. The future of AI in cybersecurity lies in custom, domain-specific solutions that are built on a foundation of trust, verifiability, and control. By leveraging technologies like Retrieval-Augmented Generation (RAG) and connecting LLMs to authoritative, enterprise-specific data sources, we can harness their power without succumbing to their flaws.