Informa TechTarget | SearchSecurity Cybersecurity Dive InformationWeek Channel Dive Explore our brands Dark Reading Resource Library Black Hat News Omdia Cybersecurity Advertise NEWSLETTER SIGN-UP Cybersecurity Topics World The Edge DR Technology Events Resources CYBERSECURITY OPERATIONS CYBER RISK APPLICATION SECURITY VULNERABILITIES & THREATS INTERVIEWS News, news analysis, and commentary on the latest trends in cybersecurity technology. Remediating Vulnerabilities With LLMs: Inside Ivanti's Automation Push Ivanti CSO Daniel Spicer says frontier models have shown surprising effectiveness in early stages; but cost and human-in-the-loop viability remain open questions. Rob Wright,Senior News Director,Dark Reading July 20, 2026 13 Min Read SOURCE: RAWINTANPIN VIA GETTY IMAGES Amid growing concern about threats actors using artificial intelligence for cyberattacks, one software vendor is finding success with deploying large-language models (LLMs) for vulnerability remediation. Last month, Ivanti disclosed CVE-2026-10520, a critical maximum-severity flaw in its Sentry mobile gateway product. But the vulnerability, which received a 10 out of 10 CVSS score, wasn't discovered by a security researcher, a third-party vendor, or even Ivanti's own engineers; rather, an LLM was the finder. Ivanti last month said it had begun using advanced models to increase the capabilities of its engineering and product security red teams to identify and fix vulnerabilities, particularly for flaws that evade "traditional tooling." This week, Daniel Spicer, chief security officer at Ivanti, gives Dark Reading a glimpse into the project, what it's achieved thus far, how Ivanti is managing the costs, and how quickly frontier model improvements are coming. Related:Cribl Adds Agentic Detection Engineering & Boosts SecOps With CardinalOps Deal Dark Reading: Can you start at the beginning of when you started using LLMs and talk about the genesis for this project? Daniel Spicer: It was mid- to late February. I was sitting down with Mike Reamer, who's also on the on the team and supports a lot of our NSG [Network Security Group] products, and it was interesting. We were both at the same time starting to realize that the 4.6 generation of models for Claude are actually pretty good, and I think that's when we were starting to realize that there was actually some potential there, not just for [app] development. We were starting to think about what we could potentially do around security, and so we kicked off an internal project the first week of March. It was a combination of engineering and security. We basically had two tracks: the first one looked at what can we do to automatically resolve vulnerabilities, and the other track, of course, is what can we do to help find vulnerabilities that we're missing. Obviously, a lot of people started getting the same ideas at the same time, because just a few weeks later, Anthropic announced Project Glasswing. It seems like they were coming to some of the same conclusions at the same time internally, and starting to talk to others in the industry who are coming to the same conclusions. But that's actually when we purchased our direct licenses with Anthropic to start coming up with these use cases. Dark Reading: And you were using the model to both find vulnerabilities and fix them? Daniel Spicer: Yes, and I see them as two different tracks, just to be clear. Finding vulnerabilities that our existing tooling misses is definitely an important track. But on the vulnerability resolution side, what ends up happening when we have findings from our SAST [static application security testing] and DAST [dynamic application security testing] scanners is they identify a weakness, and not all of the weaknesses are a vulnerability. It depends on where the weaknesses are in the code. Is it actually exposed to user control, or a certain combination of weaknesses to create an exploitable vulnerability in the product? We take a very hard stance here at Ivanti, which is to just clear out all of these weaknesses. The idea on the automatic resolution is: As we are finding these weaknesses being generated by the engineers for SAST, rather than directing the engineer to go and fix the issue, can we just grab that in advance, resolve the issue, and resubmit it to the engineer? That's really the ultimate objective there. Related:Apple Reverses Age-Old Patch Policy to Keep Up With AI Dark Reading: What have the results been since you started this LLM project? Can you say how many vulnerabilities you've found and fixed? Daniel Spicer: I can't give you exact numbers yet on what we have found. We're planning on releasing some of that research in the coming months. But on the fixed side, I can say I'm pleasantly surprised, honestly. And it's not just the Anthropic models; we're having good success with a lot of the OpenAI models as well. Related:Segmentation Works for OT If Operators Are Paying Attention Dark Reading: So you're using multiple LLMs? Daniel Spicer: Yes. We have a combination of the frontier models, as well as a few open source models that we run in special ways internally, to try to get the best outcomes. And we're not scoring those in the way that you might think. What we're really trying to do is start by taking classes of vulnerabilities, mostly by CWEs, and then create the correct prompt to enable a high-quality fix. We're trying to move this into a more permanence of operations — take that process, put it into a skill, and build it into an agent so that when the CWE is detected, we can automatically pull that ticket, pass it to the agent, have the agent fix it, and then continue into the pipeline, pass it back to our SAST scanner or our DAST scanner, and say, "Do you still see this?" And if the answer is no, then we start running it through automated performance testing, and testing for potential bugs, and getting that back into an engineer. I have a feeling that eventually there probably won't even be an engineer at the loop on that, if I'm being honest. But we started with low-complexity CWEs, and we have slowly moved into higher-complexity CWEs. Most of those we're seeing pretty high quality fixes either for things that we have found as part of track two, where our SAST tooling has missed the issue, or as part of things that we have created in the lab to help test and make sure the prompts are actually working. Dark Reading: Did this require a bit of a leap of faith to empower AI agents? There have been some notable horror stories of agents doing things they weren't supposed to do, so I'm wondering if there was some trepidation for you and the Ivanti security team going into this. Daniel Spicer: I don't know about trepidation. I think we had low expectations of success originally. I don't think any of us expected to actually be as successful as we have been. But in terms of a lot of the horror story examples of agents doing crazy things, I think there are always like some lessons there about guardrails and how you're using agents. When we talk about using an agent to operationalize this, we are giving it a PR [pull request], essentially, and that has an identified issue. And we're saying within this context, fix this. And then you're done. We're not giving it full access to GitHub or access to an OS for it to wipe. It's kind of a self-contained, short-lived harness for the specific purpose. But again, I think we are surprised about the quality that we're getting. Dark Reading: On the flip side, has seeing the effectiveness of AI agents for your purposes had an effect on your view about how these models can be used for offensive cyberattacks? Has it influenced your opinion on that topic? Daniel Spicer: [pauses] I think so. I already had opinions, and I don't know that they have dramatically shifted since I started playing with Sonnet and Opus 4.6. And GPT 5.5 and 5.6 are very impressive models as well. They have definitely closed some big gaps there. But if there was anything that really reinforced my concerns about this, it was a particular bug-bounty report that we got. And it was for an issue that we had already identified, so that's good. We had already kind of identified the weakness, but we looked at it and said, 'This looks like it came out of an Opus model' — the formatting, the certain ways it presents like an argument, and the structure. And I think that's one of the things that solidified most the power of these models for ill use. It's seeing that a $100 monthly subscription with Claude could also net you a $5,000 to $10,000 bug bounty, for example. I think that actually greatly impacts the bounty programs as they exist today. Dark Reading: On that topic, are you seeing a lot of AI-generated bug reports that are inaccurate, whether it's stuff that is 75% of the way there but not totally, or just full-on AI slop? Daniel Spicer: Last year was really bad. Last year, it was a struggle for us to meet our posted SLAs just from the amount of slop that we were getting. This year, the quality is different. And I'll go back to again around the same time in January/February where things kind of shifted. The reports that we get are, like you said, 75% to 80% of the way there or just straight up well-written reports, and you just look at it and you say, 'I'm not really sure that a human wrote this. It doesn't look quite right.' And the amount of slop has decreased. The amount of bug-bounty reports coming in has dropped significantly — it's falling off a cliff in terms of how the metrics look. And I attribute that to, we're not getting a lot of the slop that we used to get last year. Dark Reading: Why do you think that is? Daniel Spicer: I think that people aren't submitting as many reports that they believe the AI has generated correctly, and that we look as say, "This is this is terrible and makes no sense." We were getting so many reports last year. It was hundreds. Dark Reading: With the two tracks and multiple