Why generative AI is a double-edged sword for the cybersecurity sector

A lot has been fabricated from the potential for generative AI and huge language fashions (LLMs) to upend the safety trade. On the one hand, the optimistic impression is tough to disregard. These new instruments could possibly assist write and scan code, complement understaffed groups, analyze threats in actual time, and carry out a variety of different features to assist make safety groups extra correct, environment friendly and productive. In time, these instruments may be capable to take over the mundane and repetitive duties that at present’s safety analysts dread, liberating them up for the extra participating and impactful work that calls for human consideration and decision-making. 

However, generative AI and LLMs are nonetheless of their relative infancy — which implies organizations are nonetheless grappling with tips on how to use them responsibly. On prime of that, safety professionals aren’t the one ones who acknowledge the potential of generative AI. What’s good for safety professionals is commonly good for attackers as properly, and at present’s adversaries are exploring methods to make use of generative AI for their very own nefarious functions. What occurs when one thing we expect helps us begins hurting us? Will we ultimately attain a tipping level the place the expertise’s potential as a menace eclipses its potential as a useful resource?

Understanding the capabilities of generative AI and tips on how to use it responsibly shall be crucial because the expertise grows each extra superior and extra commonplace. 

Utilizing generative AI and LLMs 

It’s no overstatement to say that generative AI fashions like ChatGPT could essentially change the best way we method programming and coding. True, they aren’t able to creating code utterly from scratch (at the very least not but). However in case you have an concept for an software or program, there’s an excellent probability gen AI will help you execute it. It’s useful to consider such code as a primary draft. It might not be good, however it’s a helpful start line. And it’s lots simpler (to not point out quicker) to edit current code than to generate it from scratch. Handing these base-level duties off to a succesful AI means engineers and builders are free to interact in duties extra befitting of their expertise and experience. 

That being stated, gen AI and LLMs create output based mostly on current content material, whether or not that comes from the open web or the precise datasets that they’ve been skilled on. Which means they’re good at iterating on what got here earlier than, which could be a boon for attackers. For instance, in the identical means that AI can create iterations of content material utilizing the identical set of phrases, it might probably create malicious code that’s just like one thing that already exists, however totally different sufficient to evade detection. With this expertise, unhealthy actors will generate distinctive payloads or assaults designed to evade safety defenses which might be constructed round identified assault signatures.

A method attackers are already doing that is by utilizing AI to develop webshell variants, malicious code used to take care of persistence on compromised servers. Attackers can enter the present webshell right into a generative AI software and ask it to create iterations of the malicious code. These variants can then be used, typically together with a distant code execution vulnerability (RCE), on a compromised server to evade detection. 

LLMs and AI give solution to extra zero-day vulnerabilities and complex exploits 

Properly-financed attackers are additionally good at studying and scanning supply code to establish exploits, however this course of is time-intensive and requires a excessive degree of ability. LLMs and generative AI instruments will help such attackers, and even these much less expert, uncover and perform subtle exploits by analyzing the supply code of generally used open-source tasks or by reverse engineering industrial off-the-shelf software program.  

Typically, attackers have instruments or plugins written to automate this course of. They’re additionally extra seemingly to make use of open-source LLMs, as these don’t have the identical safety mechanisms in place to forestall this kind of malicious habits and are sometimes free to make use of. The consequence shall be an explosion within the variety of zero-day hacks and different harmful exploits, just like the MOVEit and Log4Shell vulnerabilities that enabled attackers to exfiltrate information from weak organizations. 

Sadly, the common group already has tens and even a whole bunch of hundreds of unresolved vulnerabilities lurking of their code bases. As programmers introduce AI-generated code with out scanning it for vulnerabilities, we’ll see this quantity rise as a result of poor coding practices. Naturally, nation-state attackers and different superior teams shall be able to take benefit, and generative AI instruments will make it simpler for them to take action.  

Cautiously shifting ahead 

There aren’t any straightforward options to this downside, however there are steps organizations can take to make sure they’re utilizing these new instruments in a secure and accountable means. A method to try this is to do precisely what attackers are doing: By utilizing AI instruments to scan for potential vulnerabilities of their code bases, organizations can establish doubtlessly exploitative points of their code and remediate them earlier than attackers can strike. That is significantly vital for organizations wanting to make use of gen AI instruments and LLMs to help in code era. If an AI pulls in open-source code from an current repository, it’s crucial to confirm that it isn’t bringing identified safety vulnerabilities with it. 

The considerations at present’s safety professionals have relating to the use and proliferation of generative AI and LLMs are very actual — a truth underscored by a gaggle of tech leaders recently urging an “AI pause” as a result of perceived societal threat. And whereas these instruments have the potential to make engineers and builders considerably extra productive, it’s important that at present’s organizations method their use in a rigorously thought of method, implementing the required safeguards earlier than letting AI off its metaphorical leash. 

Peter Klimek is the director of expertise throughout the Workplace of the CTO at Imperva.