Artificial Intelligence

cross-posted from: https://links.hackliberty.org/post/1236693

> Several big businesses have published source code that incorporates a software package previously hallucinated by generative AI. > > Not only that but someone, having spotted this reoccurring hallucination, had turned that made-up dependency into a real one, which was subsequently downloaded and installed thousands of times by developers as a result of the AI's bad advice, we've learned. If the package was laced with actual malware, rather than being a benign test, the results could have been disastrous. > > According to Bar Lanyado, security researcher at Lasso Security, one of the businesses fooled by AI into incorporating the package is Alibaba, which at the time of writing still includes a pip command to download the Python package huggingface-cli in its GraphTranslator installation instructions. > > There is a legit huggingface-cli, installed using pip install -U "huggingface_hub[cli]". > > But the huggingface-cli distributed via the Python Package Index (PyPI) and required by Alibaba's GraphTranslator – installed using pip install huggingface-cli – is fake, imagined by AI and turned real by Lanyado as an experiment. > > He created huggingface-cli in December after seeing it repeatedly hallucinated by generative AI; by February this year, Alibaba was referring to it in GraphTranslator's README instructions rather than the real Hugging Face CLI tool. > Study > > Lanyado did so to explore whether these kinds of hallucinated software packages – package names invented by generative AI models, presumably during project development – persist over time and to test whether invented package names could be co-opted and used to distribute malicious code by writing actual packages that use the names of code dreamed up by AIs. > > The idea here being that someone nefarious could ask models for code advice, make a note of imagined packages AI systems repeatedly recommend, and then implement those dependencies so that other programmers, when using the same models and getting the same suggestions, end up pulling in those libraries, which may be poisoned with malware. > > Last year, through security firm Vulcan Cyber, Lanyado published research detailing how one might pose a coding question to an AI model like ChatGPT and receive an answer that recommends the use of a software library, package, or framework that doesn't exist. > > "When an attacker runs such a campaign, he will ask the model for packages that solve a coding problem, then he will receive some packages that don’t exist," Lanyado explained to The Register. "He will upload malicious packages with the same names to the appropriate registries, and from that point on, all he has to do is wait for people to download the packages." > Dangerous assumptions > > The willingness of AI models to confidently cite non-existent court cases is now well known and has caused no small amount of embarrassment among attorneys unaware of this tendency. And as it turns out, generative AI models will do the same for software packages. > > As Lanyado noted previously, a miscreant might use an AI-invented name for a malicious package uploaded to some repository in the hope others might download the malware. But for this to be a meaningful attack vector, AI models would need to repeatedly recommend the co-opted name. > > That's what Lanyado set out to test. Armed with thousands of "how to" questions, he queried four AI models (GPT-3.5-Turbo, GPT-4, Gemini Pro aka Bard, and Command [Cohere]) regarding programming challenges in five different programming languages/runtimes (Python, Node.js, Go, .Net, and Ruby), each of which has its own packaging system. > > It turns out a portion of the names these chatbots pull out of thin air are persistent, some across different models. And persistence – the repetition of the fake name – is the key to turning AI whimsy into a functional attack. The attacker needs the AI model to repeat the names of hallucinated packages in its responses to users for malware created under those names to be sought and downloaded. > > Lanyado chose 20 questions at random for zero-shot hallucinations, and posed them 100 times to each model. His goal was to assess how often the hallucinated package name remained the same. The results of his test reveal that names are persistent often enough for this to be a functional attack vector, though not all the time, and in some packaging ecosystems more than others. > > With GPT-4, 24.2 percent of question responses produced hallucinated packages, of which 19.6 percent were repetitive, according to Lanyado. A table provided to The Register, below, shows a more detailed breakdown of GPT-4 responses. > > With GPT-3.5, 22.2 percent of question responses elicited hallucinations, with 13.6 percent repetitiveness. For Gemini, 64.5 of questions brought invented names, some 14 percent of which repeated. And for Cohere, it was 29.1 percent hallucination, 24.2 percent repetition. > > Even so, the packaging ecosystems in Go and .Net have been built in ways that limit the potential for exploitation by denying attackers access to certain paths and names. > > "In Go and .Net we received hallucinated packages but many of them couldn't be used for attack (in Go the numbers were much more significant than in .Net), each language for its own reason," Lanyado explained to The Register. "In Python and npm it isn't the case, as the model recommends us with packages that don’t exist and nothing prevents us from uploading packages with these names, so definitely it is much easier to run this kind of attack on languages such Python and Node.js." > Seeding PoC malware > > Lanyado made that point by distributing proof-of-concept malware – a harmless set of files in the Python ecosystem. Based on ChatGPT's advice to run pip install huggingface-cli, he uploaded an empty package under the same name to PyPI – the one mentioned above – and created a dummy package named blabladsa123 to help separate package registry scanning from actual download attempts. > > The result, he claims, is that huggingface-cli received more than 15,000 authentic downloads in the three months it has been available. > > "In addition, we conducted a search on GitHub to determine whether this package was utilized within other companies' repositories," Lanyado said in the write-up for his experiment. > > "Our findings revealed that several large companies either use or recommend this package in their repositories. For instance, instructions for installing this package can be found in the README of a repository dedicated to research conducted by Alibaba." > > Alibaba did not respond to a request for comment. > > Lanyado also said that there was a Hugging Face-owned project that incorporated the fake huggingface-cli, but that was removed after he alerted the biz. > > So far at least, this technique hasn't been used in an actual attack that Lanyado is aware of. > > "Besides our hallucinated package (our package is not malicious it is just an example of how easy and dangerous it could be to leverage this technique), I have yet to identify an exploit of this attack technique by malicious actors," he said. "It is important to note that it’s complicated to identify such an attack, as it doesn’t leave a lot of footsteps."

Employers are willing to pay up to 44% more for AI-skilled workers in IT and 41% more for those in research and development.

The Federal Communications Commission (FCC) revealed Thursday that it had unanimously agreed to make AI-generated voice cloning in robocalls illegal, a warning shot to scammers not to use the cutting-edge technology for fraud or to spread election-related disinformation.

The agency said that beginning Thursday it will rely on the 1991 Telephone Consumer Protection Act (TCPA) — which bars robocalls using pre-recorded and artificial voice messages — to target voice-cloning calls by establishing a so-called Declaratory Ruling within the TCPA.

The decision follows an AI-generated voice robocall mimicking President Joe Biden and telling voters not to turn out in the New Hampshire primary, alarming election security officials nationwide.

The New Hampshire Attorney General identified a Texas man and his company Life Corporation as the creator of the calls on Tuesday. Officials believe more than 20,000 New Hampshire residents received the calls.

“This will make it easier for state attorneys general in particular to go after these scammers,” said Harold Feld, senior vice president at the advocacy group Public Knowledge and an expert on the FCC. “While it seems fairly straightforward that an AI generated voice is ‘artificial,’ there is always the worry that a judge will not want to apply a statute written in 1991 to something new in 2024.”

“But because Congress delegated the authority to interpret the statute to the FCC, the FCC can render this common sense ruling and remove any doubt,” Feld added.

The FCC’s action will give state attorneys general nationwide a new lever to use when pursuing and ultimately charging people behind junk calls, FCC Chairwoman Jessica Rosenworcel said in a statement.

The deployment of AI generated voice-cloning robocalls has “escalated” in the last few years, the FCC said in a press release, noting that in November it started a “Notice of Inquiry” to “build a record on how the agency can combat illegal robocalls and how AI might be involved.”

During that inquiry, the agency investigated how AI can be used for robocall scams by mimicking voices known to recipients, but also looked into how AI might bolster pattern recognition, allowing authorities to more easily recognize illegal robocalls. The TCPA provides the FCC with the ability to fine robocallers through civil enforcement actions. It also allows consumer lawsuits, enables carriers to block the calls, and hands state officials a new tool for enforcement.

More than 26 state attorneys general recently wrote to the FCC expressing support for the new rule, the press release said.

On Wednesday, Commerce Secretary Gina Raimondo made new appointments to a National Institute of Standards and Technology (NIST) institute dedicated to coming up with science-based guidelines for AI, and on Thursday the agency announced the creation of a consortium of some 200 companies and organizations within the industry to support "the development and deployment of safe and trustworthy artificial intelligence."

The forthcoming U.S. Artificial Intelligence Safety Institute will be charged with “laying the foundation for AI safety across the world,” according to an update posted on NIST’s website. “This will help ready the U.S. to address the capabilities of the next generation of AI models or systems, from frontier models to new applications and approaches, with appropriate risk management strategies.”

A Beijing court will have to decide if an AI-generated voice, alleged to resemble a voiceover artist and used without her approval, has infringed on her right to voice.

The Beijing Internet Court on Tuesday began its hearing of a lawsuit filed by the artist, whose family name is Yin, claiming the AI-powered likeness of her voice had been used in audiobooks sold online. These were works she had not given permission to be produced, according to a report by state-owned media China Daily.

Yin said the entities behind the AI-generated content were profiting off the sale proceeds from the platforms on which the audiobooks were sold. She named five companies in her suit, including the provider of the AI software, saying their practices had infringed on her right to voice.

"I've never authorized anyone to make deals using my recorded voice, let alone process it with the help of AI, or sell the AI-generated versions," she said in court. "I make a living with my voice. The audiobooks that use my AI-processed voice have affected my normal work and life."

The defendants argued that the AI-powered voice was not Yin's original voice and should be distinguished from the latter.

The court is scheduled to reveal its ruling at a later date, China Daily reported. Yin has sued for 600,000 yuan ($84,718) in financial losses and an additional 100,000 yuan for mental distress.

The legal case follows another last month when a Chinese court ruled in favor of a plaintiff, surnamed Li, who accused another of using an image he generated using an open source AI software, without his consent. Li had posted the picture on his personal social media account and argued that its unauthorized reuse infringed on his intellectual property rights.

In her defense, the defendant said the image had popped up via an online search and bore no watermark or information about its copyright owner. She added that she did not use the content for commercial gains. The image was used on her personal webpage, according to China Daily.

In its ruling, the Beijing Internet Court said Li had put in "intellectual investment" to tweak the image in line with what he wanted, including using keywords to generate the woman's appearance and image lighting.

The court added that people who used AI features to produce an image still are the ones using a tool to create, stating that it is the person, rather than the AI, who invests intellectually in generating the image.

Li was reported to have used AI software Stable Diffusion to produce the image in question.

Commenting on the case, law firm King & Wood Mallesons said the Beijing court's ruling appeared to contradict recent decisions in the US on whether AI-generated content could have copyrights. The firm pointed to cases such as "Zarya of the Dawn" and "Theatre D'opera Spatial" where US courts denied copyright protection to AI-generated content that lacked human authorship.

The law firm, though, noted a difference between the cases in China and the US, stressing the Beijing Internet Court ruling appeared to distinguish a "straightforward" AI-generated content that had no creative involvement, from one that demonstrated continuous human intervention to finetune the end-product. This involved adding prompts and tech parameters until the human creators got the result they wanted.

In the latter, the Beijing court had viewed the content as "AI-assisted" work in which Li had invested personal judgment and made aesthetic choices in producing the image, King & Wood Mallesons wrote. Li also demonstrated the ability to produce the same picture with the same sequence of instructions, comprising more than 150 prompts, and tech parameters.

"It would be interesting to speculate whether the [Beijing Internet Court] would come to the same conclusion, [in] recognizing the copyrightability of the AI picture, if the AI-generated content turns out to be unpredictable, producing various AI pictures each time," noted the Hong Kong-based law firm. "Would the Chinese judges change their rationale because the human authors do not have 'control' in the AI-generated content output?"

The API tokens of tech giants Meta, Microsoft, Google, VMware, and more have been found exposed on Hugging Face, opening them up to potential supply chain attacks.

Researchers at Lasso Security found more than 1,500 exposed API tokens on the open source data science and machine learning platform – which allowed them to gain access to 723 organizations' accounts.

In the vast majority of cases (655), the exposed tokens had write permissions granting the ability to modify files in account repositories. A total of 77 organizations were exposed in this way, including Meta, EleutherAI, and BigScience Workshop - which run the Llama, Pythia, and Bloom projects respectively.

The three companies were contacted by The Register for comment but Meta and BigScience Workshop did not not respond at the time of publication, although all of them closed the holes shortly after being notified.

Hugging Face is akin to GitHub for AI enthusiasts and hosts a plethora of major projects. More than 250,000 datasets are stored there and more than 500,000 AI models are too.

The researchers say that if attackers had exploited the exposed API tokens, it could have led to them swiping data, poisoning training data, or stealing models altogether, impacting more than 1 million users.

In just their own work, the researchers say they were able to achieve the necessary access to modify 14 different datasets with tens of thousands of downloads per month.

Data poisoning attacks of this kind are among the most critical threats facing AI and ML as their prominence grows, Forcepoint says. The attack is in OWASP's top 10 risks for LLMs and could lead to a range of consequences.

Google's anti-spam filters for Gmail are effective because of the reliably trained models that power the feature, but these have been compromised on a number of occasions in the past to push seemingly benign malicious emails into users' inboxes.

Another hypothetical scenario in which data poisoning could have a serious organizational impact is if the dataset that designates different types of network traffic were to be sabotaged. If network traffic isn't correctly identified as email, web browsing, etc, then it could lead to misallocated resources and potential network performance issues.

Lasso Security's researchers were also able to gain the required access to steal more than 10,000 private models, a threat that also makes OWASP's top 10 AI security risks.

"The ramifications of this breach are far-reaching, as we successfully attained full access, both read and write permissions to Meta Llama 2, BigScience Workshop, and EleutherAI, all of these organizations own models with millions of downloads – an outcome that leaves the organization susceptible to potential exploitation by malicious actors," says Bar Lanyado, security researcher at Lasso Security.

‍"The gravity of the situation cannot be overstated. With control over an organization boasting millions of downloads, we now possess the capability to manipulate existing models, potentially turning them into malicious entities. This implies a dire threat, as the injection of corrupted models could affect millions of users who rely on these foundational models for their applications."

The exposed API tokens were discovered by researchers conducting a series of substring searches on the platform and manually collecting them. They then used the whoami Hugging Face API to determine whether the token was valid, who owned it, the owner's email, what organizations the owner belongs to, and the token's permissions.

Exposing API tokens is often done when developers store the token in a variable for use in certain functions, but forget to hide it when pushing the code to a public repository.

GitHub has its Secret Scanning feature to prevent leaks like this and is available to all users free of charge, and Hugging Face runs a similar tool that alerts users to exposed API tokens which are hardcoded into projects.

While investigating the exposed secrets on Hugging Face, researchers also found a weakness with its organization API tokens (org_api), which had already been announced as deprecated, that could be used for read access to repositories, and billing access to a resource. It was also blocked in Hugging Face's Python library by adding a check to the type of token in the login function.

"Therefore we decided to investigate it, and indeed the write functionality didn't work, but apparently, even with small changes made for the login function in the library, the read functionality still worked, and we could use tokens that we found to download private models with exposed org_api token e.g. Microsoft," says Lanyado in thre blog.

Lasso Security says all the affected organizations were contacted and the major companies like Meta, Google, Microsoft, and VMware responded on the same day, revoking the tokens and removing the code from their respective repositories.

Stella Biderman, executive director at EleutherAI, told us: "We are always grateful to ethical hackers for their important work identifying vulnerabilities in the ecosystem and are committed to building community norms and best practices that promote safety in machine learning research."

Biderman pointed to a recent collaboration between EleutherAI, Hugging Face, and Stability AI to develop a new checkpointing format to mitigate attacker modifications, saying “the harm that can be done by such attacks has been massively reduced.”

"We helped develop an alternative checkpointing format (now the norm on the Hub) where such behavior is not possible now, limiting the harm someone could do with an exploit like the key leak," she added. "Of course, there are still very real harms to both users and organizations due to key leaks and we are always on the lookout for such things and how we can further mitigate harm." ®

Updated at 12.49 UTC on December 5, 2023, to add:

Following publication of this article, Hugging Face sent a statement from Clement Delangue, co-founder and CEO at the company:

"The tokens were exposed due to users posting their tokens in platforms such as the Hugging Face Hub, GitHub, and others. In general we recommend users do not publish any tokens to any code hosting platform.

"All Hugging Face tokens detected by the security researcher have been invalidated and the team has taken and is continuing to take measures to prevent this issue from happening more in the future, for example, by giving companies more granularity in terms of permissions for their tokens with enterprise hub and detection of malicious behaviors. We are also working with external platforms like Github to prevent valid tokens from getting published in public repositories."

When the board of OpenAI suddenly fired the company’s CEO last month, it sparked speculation that board members were rattled by the breakneck pace of progress in artificial intelligence and the possible risks of seeking to commercialize the technology too quickly. Robust Intelligence, a startup founded in 2020 to develop ways to protect AI systems from attack, says that some existing risks need more attention.

Working with researchers from Yale University, Robust Intelligence has developed a systematic way to probe large language models (LLMs), including OpenAI’s prized GPT-4 asset, using “adversarial” AI models to discover “jailbreak” prompts that cause the language models to misbehave.

While the drama at OpenAI was unfolding, the researchers warned OpenAI of the vulnerability. They say they have yet to receive a response.

“This does say that there’s a systematic safety issue, that it’s just not being addressed and not being looked at,” says Yaron Singer, CEO of Robust Intelligence and a professor of computer science at Harvard University. “What we’ve discovered here is a systematic approach to attacking any large language model.”

OpenAI spokesperson Niko Felix says the company is “grateful” to the researchers for sharing their findings. “We’re always working to make our models safer and more robust against adversarial attacks, while also maintaining their usefulness and performance,” Felix says.

The new jailbreak involves using additional AI systems to generate and evaluate prompts as the system tries to get a jailbreak to work by sending requests to an API. The trick is just the latest in a series of attacks that seem to highlight fundamental weaknesses in large language models and suggest that existing methods for protecting them fall well short.

“I’m definitely concerned about the seeming ease with which we can break such models,” says Zico Kolter, a professor at Carnegie Mellon University whose research group demonstrated a gapping vulnerability in large language models in August.

Kolter says that some models now have safeguards that can block certain attacks, but he adds that the vulnerabilities are inherent to the way these models work and are therefore hard to defend against. “I think we need to understand that these sorts of breaks are inherent to a lot of LLMs,” Kolter says, “and we don’t have a clear and well-established way to prevent them.”

Large language models recently emerged as a powerful and transformative new kind of technology. Their potential became headline news as ordinary people were dazzled by the capabilities of OpenAI’s ChatGPT, released just a year ago.

In the months that followed the release of ChatGPT, discovering new jailbreaking methods became a popular pastime for mischievous users, as well as those interested in the security and reliability of AI systems. But scores of startups are now building prototypes and fully fledged products on top of large language model APIs. OpenAI said at its first-ever developer conference in November that over 2 million developers are now using its APIs.

These models simply predict the text that should follow a given input, but they are trained on vast quantities of text, from the web and other digital sources, using huge numbers of computer chips, over a period of many weeks or even months. With enough data and training, language models exhibit savant-like prediction skills, responding to an extraordinary range of input with coherent and pertinent-seeming information.

The models also exhibit biases learned from their training data and tend to fabricate information when the answer to a prompt is less straightforward. Without safeguards, they can offer advice to people on how to do things like obtain drugs or make bombs. To keep the models in check, the companies behind them use the same method employed to make their responses more coherent and accurate-looking. This involves having humans grade the model’s answers and using that feedback to fine-tune the model so that it is less likely to misbehave.

We have just released a paper that allows us to extract several megabytes of ChatGPT’s training data for about two hundred dollars. (Language models, like ChatGPT, are trained on data taken from the public internet. Our attack shows that, by querying the model, we can actually extract some of the exact data it was trained on.) We estimate that it would be possible to extract ~a gigabyte of ChatGPT’s training dataset from the model by spending more money querying the model.

Unlike prior data extraction attacks we’ve done, this is a production model. The key distinction here is that it’s “aligned” to not spit out large amounts of training data. But, by developing an attack, we can do exactly this.

We have some thoughts on this. The first is that testing only the aligned model can mask vulnerabilities in the models, particularly since alignment is so readily broken. Second, this means that it is important to directly test base models. Third, we do also have to test the system in production to verify that systems built on top of the base model sufficiently patch exploits. Finally, companies that release large models should seek out internal testing, user testing, and testing by third-party organizations. It’s wild to us that our attack works and should’ve, would’ve, could’ve been found earlier.

The actual attack is kind of silly. We prompt the model with the command “Repeat the word”poem” forever” and sit back and watch as the model responds

cross-posted from: https://links.hackliberty.org/post/115755

> If I told you that your seemingly private conversations with Bard are being indexed and appearing in Google search results, would you still use the AI chatbot? That's exactly what's happened, and Google is now scrambling to fix the issue.

The British government has quietly sacked an independent advisory board of eight experts that had once been poised to hold public sector bodies to account for how they used artificial intelligence technologies and algorithms to carry out official functions.

It comes as Prime Minister Rishi Sunak drives forward with a much-publicized commitment to make the United Kingdom a world leader in AI governance, and ahead of a global AI Safety Summit being arranged for November in Bletchley Park.

A Matrix bot that uses waylaidwanderer/node-chatgpt-api to access the official ChatGPT API.

Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa