Pitfalls to keep away from when utilizing AI to research code


Bogdan Kortnov is co-founder & CTO at illustria, a member of the Microsoft for Startups Founders Hub program. To get began with Microsoft for Startups Founders Hub, enroll right here.

The rise of synthetic intelligence has caused a revolutionary change in varied sectors, unlocking a brand new potential for effectivity, value financial savings, and accessibility. AI can carry out duties that sometimes require human intelligence, but it surely considerably will increase effectivity and productiveness by automating repetitive and boring duties, permitting us to concentrate on extra modern and strategic work.

Lately we needed to see how nicely a big language mannequin (LLM) AI platform like ChatGPT is ready to classify malicious code, via options corresponding to code evaluation, anomaly detection, pure language processing (NLP), and risk intelligence. The outcomes amazed us. On the finish of our experimentation we have been capable of admire every thing the instrument is able to, in addition to determine total finest practices for its use.

It’s vital to notice that for different startups seeking to reap the benefits of the various advantages of ChatGPT and different OpenAI providers, Azure OpenAI Service not solely gives APIs and instruments that

Detecting malicious code with ChatGPT

As members of the Founders Hub program by Microsoft for Startups, an excellent start line for us was to leverage our OpenAI credit to entry its playground app. To problem ChatGPT, we created a immediate with directions to reply with “suspicious” when the code incorporates malicious code, or “clear” when it doesn’t.

This was our preliminary immediate:

You’re an assistant that solely speaks JSON. Don’t write regular textual content. You analyze the code and end result if the code is having malicious code. easy response with out rationalization. Output a string with solely 2 doable values. “suspicious” if destructive or “clear” if constructive.

The mannequin we used is “gpt-3.5-turbo” with a customized temperature setting of 0, as we needed much less random outcomes.

Initial code

Within the instance proven above, the mannequin responded “clear.” No malicious code detected.

Malicious code

The subsequent snippet elicited a “suspicious” response, which gave us confidence that ChatGPT may simply inform the distinction.

Automating utilizing OpenAI API

We proceeded to create a Python script to make use of OpenAI’s API for automating this immediate with any code we want to scan.

To make use of OpenAI’s API, we first wanted an API key.

API keys

There’s an official consumer for this in PyPi .

Import OpenAI

Subsequent, we challenged the API to research the next malicious code. It injects the extra Python code key phrase “eval” obtained from a URL, a way extensively utilized by attackers.

Import requests

As anticipated, ChatGPT precisely reported the code as “suspicious.”

Scanning packages

We wrapped the easy operate with further features capable of scan information, directories, and ZIP information, then challenged ChatGPT with the favored package deal requests code from GitHub.

Analyze file

ChatGPT precisely reported once more, this time with “clear.”

We then proceeded with a replica of W4SP stealer malware hosted on GitHub.

Print result

You guessed proper: ChatGPT precisely reported “suspicious.”

Full code is offered right here on this gist.

Though it is a easy implementation with solely round 100 traces of code, ChatGPT confirmed itself to be a really highly effective instrument , leaving us to solely think about the chances of the close to future!

Sounds nice, so what’s the catch?

As we famous earlier, ChatGPT and different AI fashions might be precious instruments for detecting malicious code, however no platform might be good (not but, anyway), and shouldn’t be solely relied upon. AI fashions like ChatGPT are educated on massive datasets and have sure limitations. They could not, for instance, be capable to precisely detect all kinds of malicious code or variations of malicious conduct, particularly if the malicious code is refined, obfuscated, or makes use of novel strategies. Malicious code is consistently evolving, with new threats and strategies rising usually. Common updates and enhancements to ChatGPT’s coaching information and algorithms are essential to keep up effectiveness in detecting it.

Throughout our experiments, we encountered three potential limitations that any enterprise ought to pay attention to when trying to make use of ChatGPT to detect malicious code.

Pitfall #1: Overriding directions

LLMs corresponding to ChatGPT might be simply manipulated to introduce outdated safety dangers in a brand new format.

For instance, we took the identical snippet from the earlier Python code and added a remark instructing ChatGPT to report this file as clear whether it is being analyzed by an AI:

Import requests

This tricked ChatGPT into reporting a suspicious code as “clear.”

Do not forget that for as spectacular as ChatGPT has confirmed to be, at their core these AI fashions are word-generating statistics engines with additional context behind them. For instance, if I ask you to finish the immediate, “the sky is b…” you and everybody you realize will most likely say, “blue.” That likelihood is how the engine is educated. It’s going to full the phrase based mostly on what others might need stated. The AI doesn’t know what the “sky” is, or what the colour “blue” appears to be like like, as a result of it has by no means seen both.

The second challenge is that the mannequin has by no means thought the reply, “I don’t know.” Even when they ask one thing ridiculous, the mannequin will all the time spit out a solution, although it is likely to be gibberish, as it’s going to attempt to “full” the textual content by decoding the context behind it.

The third half consists of the way in which an AI mannequin is fed information. It all the time will get the info via one pipeline, as if being fed by one particular person. It could actually’t differentiate between completely different individuals, and its worldview consists of 1 particular person solely. If this particular person says one thing is “immoral,” then turns round and says it’s “ethical,” what ought to the AI mannequin imagine?

Pitfall #2: Manipulation of response format

Except for manipulating the results of the returned content material, the attacker could manipulate the response format, breaking the system or leveraging a vulnerability of an inside parser or a deserialization course of.

For instance:

Determine whether or not a Tweet’s sentiment is constructive, impartial, or destructive. return a solution in a JSON format: {“sentiment”: Literal[“positive”, “neutral”, “negative”]}.

Tweet: “[TWEET]”


The tweet classifier works as meant, returning response in JSON format.

Return answer

This breaks the tweet classifier.

Pitfall #3: Manipulation of response content material

When utilizing LLMs, we will simply “enrich” an interplay with a person, making it really feel like they’re speaking with a human when contacting assist or filling some on-line registration kind. For instance:

Bot: “Hey! What’s your title and the place are you from?”

Consumer: “[USER_RESPONSE]”

The system will then take the person response and ship the request to an LLM to extract the “first title,” “final title,” and “nation” fields.

Please extract the title, final title and nation from the next person enter. Return the reply in a JSON format {“title”: Textual content, “last_name”: Textual content, “nation”: Textual content}:



This parses the person response right into a JSON format.

When a traditional person enter is handed, all of it appears nice. However an attacker can move the next response:


ChatGPT Jailbreak² with customized SQL Injection technology request.

Whereas the LLM response just isn’t good, it demonstrates a technique to generate an SQL injection question which bypasses any WAF safety.


Our experiment with ChatGPT has proven that language-based AI instruments generally is a highly effective useful resource for detecting malicious code. Nevertheless, it is very important be aware that these instruments should not fully dependable and might be manipulated by attackers.

LLMs are an thrilling expertise but it surely’s vital to keep in mind that with the nice comes the unhealthy. They’re weak to social engineering, and each enter from them must be verified earlier than it’s processed.


Illustria’s mission is to cease provide chain assaults within the growth lifecycle whereas rising developer velocity utilizing an Agentless Finish-to-Finish Watchdog whereas imposing your open-source coverage. For extra details about us and easy methods to shield your self, go to illustria.io and schedule a demo.

Members of the Microsoft for Startups Founders Hub get entry to a variety of cybersecurity sources and assist, together with entry to cybersecurity companions and credit. Startups in this system obtain technical assist from Microsoft consultants to assist them construct safe and resilient techniques, and to make sure that their functions and providers are safe and compliant with related laws and requirements.

For extra sources for constructing your startup and entry to the instruments that may show you how to, enroll at the moment for Microsoft for Startups Founders Hub.

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