A malicious ChatGPT share link could run an attacker's code in the logged-in session of anyone who opened it, allowing them to read their private conversations, researchers at the security firm Parameter showed. The researchers reported the flaw through OpenAI's bug bounty program on October 18, and OpenAI fixed it eight days later and rated it P1, its most serious category.
Yoba malware uses fake ChatGPT and Claude apps to sideload malware through signed installers and deploy a cryptocurrency miner. Check Point Research has spent two months tracking a campaign it calls Yoba, which uses the popularity of AI tools to spread a custom malware chain. The attackers build fake GitHub projects that pose as AI desktop applications such as GPT-5.6 Sol, GPT-6 Astra and Claude Opus. The projects look convincing and run on real GitHub infrastructure, which helps them seem legitimate to potential victims. “These projects look convincing and use real GitHub infrastructure, which helps them appear legitimate to potential victims. The website exhibits search-targeted content and suspicious installer-delivery tactics consistent with an SEO-poisoning lure. Inside the installers, there is a malicious component we refer to as the YobaDropper. This dropper includes another component, the YobaLoader, which is responsible for contacting attacker-controlled infrastructure and fetching the final payload.” reads the report. “In this report, we describe how victims are lured to these fake projects, how the YobaDropper and YobaLoader work together, and how the attackers ultimately use this chain to run unwanted software on infected systems.” The lure is simple. Each project has a repository, a GitHub.io page describing the software, and a ZIP release with the malware. The repositories show no fake stars or forks, yet they still rank among the top Google results, most likely through SEO poisoning that optimizes titles, descriptions and hosting domains. The first repository appeared around July 2026, but the first malicious releases landed only on August 30 and 31, and a third followed about five days later on September 4. Each repository uses a different Outlook address, and commit times point to UTC+4, UTC-4 and UTC+3, which Check Point reads as a deliberate attempt to hide the actor’s real time zone. Here’s the trick that gives the report its title. Each ZIP holds a legitimate signed executable from OpenAI or Anthropic, named Setup.exe, next to a malicious winmm.dll. The signed program loads the DLL on its own, a technique known as DLL sideloading, which is often used to bypass security protections. The ZIP files were not even password-protected. Attackers often use passwords to prevent security tools from automatically unpacking and scanning files. However, these binaries already had very low detection rates because they used anti-sandbox and other techniques to avoid analysis. The DLL’s compilation date is another clue: it was built on the same day it was added to the archive, unlike the legitimate files around it. The dropper, called YobaDropper, checks the environment before running. It calculates a hash of the name of the program that launched it and continues only if the name is Setup.exe. This means that sandboxes that run the DLL on its own will fail this check. When that happens, the process does not exit. Instead, it sleeps 200,000 times for 5.5 seconds each, a total of about 12.73 days. The malware also avoids victims in CIS countries by checking the keyboard layout. It excludes Russian, Belarusian, Tajik, Armenian, Azerbaijani, Georgian, Kazakh, Kyrgyz, Turkmen, Uzbek, and Tatar. Interestingly, Ukraine, which has often appeared in similar exclusion lists, is not included. Next, the malware requests administrator privileges. If the user does not have them, it displays the message “Please run this program as Administrator.” and exits. The victim must then restart it with administrator rights. Once it has the required privileges, the malware creates a folder called WMProcess under ProgramData. It copies the legitimate WMPDMC.exe file from System32 into this folder and places the second-stage payload there under the name wmpdui.dll. The malicious code achieves persistence by using a scheduled task dressed up as Microsoft. The author is set to Microsoft Corporation, the description says it collects device metadata for Windows Media Player, and it starts three minutes after boot as Local System. The task launches the legitimate program, which sideloads the malicious DLL. The second stage, YobaLoader, shares code with the dropper, which points to the same developer. It only runs under WMPDMC.exe and uses a different seed for its hashing than the dropper does, which separates the stages and weakens hash-based detection. Its job is to find its payload without a classic command server. It goes to a Telegram channel, t[.]me/yoba_fanclub, and downloads the channel’s profile picture. The last 50 bytes of the image seed a domain generator that builds names from word lists in the form adjective-noun-verb-number on dedyn[.]io. Change the picture and the operator gets a new domain. Then it asks Google’s DNS-over-HTTPS service for a TXT record on that domain. The reply is signed, and the loader checks the signature before decrypting it, which gives a link to a payload on Catbox. Check Point says this reliance on DoH and common hosting aims to bypass passive DNS monitoring and some gateway protections, and the campaign avoids dedicated command-and-control infrastructure, using Telegram, Tor, Catbox and DoH instead. The operator can choose how the payload runs. It can inject it into svchost.exe, save it as an executable after removing the Zone.Identifier mark, or run it as a hidden PowerShell script while bypassing execution policies. “Finally, the injected payload into svchost.exe is an XMRig 6.26.0 miner with a hidden command line targeting a TLS-enabled onion mining endpoint through a local Tor SOCKS5 proxy. It can unpack and start Tor 0.4.9.12 plus lyrebird 0.8.1, monitor user-input activity to pause mining, and conditionally load the WinRing0 driver service for MSR-based mining optimization.” continnues the report. The miner uses Tor to connect to an encrypted mining server. It can start its own Tor components, use about 85% of CPU threads, and pause mining when the user is active. It resumes after 240 seconds of inactivity and can load the WinRing0 driver to optimize mining performance. However, YobaLoader is not limited to cryptocurrency mining. Its DNS tasking system allows operators to deliver any PE executable or PowerShell payload, meaning future attacks could distribute other malware families. Earlier versions appeared in June 2026. They used Cursor.exe for sideloading, a startup shortcut for persistence, and excluded only Russia. They also used .aber[.]ir instead of dedyn[.]io and included anti-debugging and anti-VM checks that later versions dropped. Check Point believes the attackers may have Russian origins and mainly operate in the UTC-4 time zone, but this is an assessment, not a confirmed attribution. Defenders should look for suspicious WMPDMC.exe copies under ProgramData, fake Microsoft scheduled tasks, Telegram-related traffic, DNS lookups for dedyn[.]io through dns.google, and unexplained high CPU usage. “The Yoba campaign shows how attackers can use trusted platforms and the current interest in AI tools to distribute malware in a way that looks believable and harmless at first glance. By packaging their code together with well-known software names and hosting it on GitHub, they lower the chances that users or basic security checks will immediately flag the files as suspicious.” concludes the report. “Organizations should be cautious about installing “AI helpers” or similar tools from unverified repositories and should enforce policies that restrict running software from unknown sources. Combining user awareness with basic technical controls.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Yoba malware)
Security researchers at Anthropic have developed an exploit for a critical Linux kernel vulnerability that allows unauthenticated attackers to remotely execute commands as root on servers running Multipath TCP (MPTCP) listeners. The flaw, tracked as CVE-2026-98432, also affects ordinary TCP connections, although remote code execution has not been publicly demonstrated on servers without an MPTCP listener. Multipath TCP (MPTCP) is a TCP feature that lets a single connection use multiple network paths at the same time to improve performance and reliability. It is commonly used in environments where devices have multiple network connections, such as Wi-Fi and cellular networks. CVE-2026-98432 is a use-after-free vulnerability in the kernel's IPv4 TCP networking code that received a severity score of 9.8 out of 10 from the Linux kernel CVE team.
Hardware wallet device maker Trezor has received credible reports about a suspected hardware crypto-theft implant in a Trezor Safe 5 sold by its reseller CryptoBilis in Southeast Asia. Although the company has not yet received the device to examine it and confirm whether there is a malicious implant, it has warned potentially affected users to move their private keys and digital assets to known safe wallets. Trezor wallets are physical devices that store cryptocurrency private keys and authorize transactions. Safe 5 is one of its latest models and is currently available for purchase from the official store. According to Trezor, the concerns arose after it received a report from an Indonesian user who purchased a Safe 5 device from CryptoBilis through Tokopedia in August 2026. The user opened it and found an extra circuit board between the wallet's main board and screen, equipped with a cellular module and antenna.
A malicious version of Anthropic's Claude Code VS Code extension was distributed through Cursor's extension marketplace for approximately 11 hours on October 29, exposing up to 5,000 developers to malware designed to install persistent SSH backdoors.
Attackers have started exploiting a maximum-severity GitLab vulnerability patched earlier this month, according to the US Cybersecurity and Infrastructure Security Agency (CISA). Tracked as CVE-2026-97229, this critical security flaw was discovered by a security researcher using the 'joaxcar' handle in the CI/CD Regular Expression Compiler, which parses, compiles, and evaluates regular expressions in pipeline configuration files. CVE-2026-97229 stems from an integer overflow weakness and could let remote attackers run arbitrary code on targeted instances via a maliciously crafted regular expression in a CI/CD configuration, due to insufficient expression validation. "GitLab has remediated an issue that, under certain conditions, could have allowed an unauthenticated user to execute arbitrary code on the GitLab server via a specially crafted regular expression in a CI/CD configuration, due to insufficient regular expression validation," it said.
A practical pentest workflow combines CVSS, EPSS and business impact to validate findings, prioritize fixes and document remediation decisions. Remediation prioritization after a pentest is the process of ranking and ordering security findings based on their risk, exploitability, and business impact. A penetration test can produce any number of findings, ranging from the very low (zero) to the very high (hundreds). The security of the tested organization depends on the findings management processes addressing the most important results first. CVSS alone misranks risk. Accurate prioritization relies on combining CVSS, EPSS, and business context. 68.4% of practitioners say a 500+ candidate finding set would strain the team or be unmanageable, so it’s important to validate first. 81.7% of practitioners see AI-generated findings that need significant manual validation, so validation with concrete proof remains essential. Document every deprioritization, because that record counts as audit evidence. Why aren’t CVSS scores alone enough to inform remediation? When prioritizing pentest results, some practitioners rely solely on frameworks like the Common Vulnerability Scoring System (CVSS). The logic is sound: the higher a vulnerability’s CVSS score is, the more risk it poses, and the sooner you should fix it, right? Unfortunately, the reality is much more complicated. Boiling down a complex ecosystem of risk into a one-dimensional number means ignoring the wider business context. In the end, that means you fix vulnerabilities that pose serious theoretical risk first, while genuine exposures get pushed to the back of the queue. Lets use two hypothetical vulnerabilities as an example. Vulnerability A is an RCE flaw, CVSS 9.8, on an isolated intranet site. Vulnerability B is a BOLA flaw, CVSS 6.5, on a public facing production payment API. Now think about which one you would fix first. Would you prioritize the“critical” flaw on an intranet site, containing no sensitive data that attackers could only reach if they’d already compromised your internal network? Or would you prioritize the “medium” flaw on an API that processes live credit card transactions, and that any script kiddie could manipulate? Across an entire organization, or multiple organizations if you’re a consultant or MSP, those critical flaws can really pile up. In one AI pentesting survey of 158 base practitioners by Pentest-Tools.com, conducted in June 2026, 68.4% of respondents said a 500+ candidate finding set would strain the team or be unmanageable. Approaching risk prioritization by trying to fix every “Critical” finding is going to exhaust you and your team. That’s when people make mistakes, and you end up with a major incident on your hands. What separates a validated finding from a candidate? That same distinction, in part, separates a candidate finding from a validated one. For example, vulnerability with a high CVSS score but near-zero exploitability should remain a candidate. To graduate a vulnerability from a candidate to a legally and technically validated finding, it must pass through a strict filter that involves three processes. The first step is to eliminate as much administrative noise as possible. That means deduplicating findings and removing any that are obviously non-exploitable. Then, you need to confirm exploitability. That typically means executing a safe, controlled attack path against the target environment to prove that a state change, data exposure, or privilege escalation can actually occur. Finally, you need concrete evidence. A validated finding cannot rely on a generic tool’s “confidence score.” It must include comprehensive proof that a developer or auditor can immediately follow: Technical proof tends to include HTTP request/response traces or specific error payloads. Proof of impact includes things like screenshots or server-side telemetry demonstrating unauthorized data access. Now, many vendors are going to claim that AI-generated findings ease the strain of exploit validation. In many cases, they’re telling the truth. But as with any AI technology, a healthy level of skepticism is important. According to the same study, 81.7% of respondents encounter AI-generated findings that need significant manual exploit validation at least sometimes. How do CVSS, EPSS and business context combine into a ranking? Together, CVSS, Exploit Prediction Scoring System (EPSS) scores, and business context create a single risk ranking that reflects real-world danger to the tested organization. CVSS, as mentioned earlier, measures theoretical severity. It tells you how bad a flaw is in a vacuum, but is entirely blind to context. EPSS measures probability. It tells you how likely an attacker is to exploit a flaw in the wild over the next 30 days, but doesn’t measure the potential impact of an exploitation. Business context measures operational impact. It tells you the value of the affected asset, how easily an attacker can reach a flaw, and what they can steal. It tells you nothing, however, about the threat itself. Ultimately, business context is expressed as residual risk, the danger left over after you factor in your existing defenses. Calculating it relies on measuring four factors: Exposure: Whether a server is open to the public internet. User count: How many people use a system. Data sensitivity: Whether the system holds sensitive information. Compensating controls: Whether you have firewalls or antivirus tools actively blocking the attack path. By checking these boxes, you won’t waste time fixing a “Critical” bug locked in an empty, disconnected server. Instead, you’ll prioritize the “Medium” bug on your public checkout page that hackers are actively trying to breach. What goes first when dev time is limited? Sometimes, you’re not going to have much time for remediation. That’s natural. In those situations, prioritize validated findings with high threat activity (EPSS score) and critical business exposure. They’re the findings that pose the most threat to your organization. Prioritization done well is itself evidence. A ranked, validated list, with a written reason for what was deprioritized, is what a team can defend later. You’ll need to record the owner (the specific individual accepting the risk), the date of the decision, the technical operational basis for deprioritization (such as an active compensating control), and a hard review date for re-evaluation. Why does the ranked list count as audit evidence? A prioritized ranking list counts as valid audit evidence because it proves compliance alignment with several major security frameworks. For example: ISO 27001 A.8.8 demands that organizations evaluate real-world exposure, take targeted corrective actions, and retain objective evidence that the remediation loop has successfully closed. PCI DSS 11.4 mandates that penetration testing actively validates and exploits findings, with formal retesting conducted after significant organizational changes. SOC 2 CC7.1 relies on this documentation to confirm that an organization maintains systemic operational consistency and rigorous vulnerability triage over time. A ranked, validated list also gives MSPs and MSSPs a clear, auditable record of truth that clients can either accept or challenge with their own evidence. This transparency ensures both sides agree on which real-world risks need to be addressed first. What does a defensible ranking record contain? A defensible ranking record contains the validated asset identification, its technical CVSS severity, its real-world EPSS probability, the active compensating controls defining its residual list, the formal sign-off from the risk owner, and the targeted review schedule. About the Author: Josh Breaker-Rolfe is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He’s written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, pentest)
Drupal warned admins to patch a critical access bypass vulnerability in the Commerce Core module that attackers are exploiting in the wild. Tracked as CVE-2026-118539, this flaw stems from a missing authorization weakness that can be abused by unauthenticated attackers and affects Commerce Core versions 0.0.0 through 2.41 and 3.0.0 through 3.3.13. The Drupal security team asked admins on the 3.x branch to upgrade immediately to Commerce Core 3.3.13 (or Commerce Core 8.x-2.41 for earlier versions) and audit orders for inconsistencies, since threat actors are exploiting this vulnerability in attacks. "This module enables you to sell goods and services to customers through an included shopping cart and checkout form or through decoupled applications via APIs," Drupal said in a Wednesday advisory tagged as highly critical.
A modern organization protects its information systems with a whole suite of technologies that generate thousands of security alerts every day. Looking at these alerts in bulk, you’ll see that a significant share are false positives. Ideally, these alerts would be filtered out automatically so that analysts could focus on the ones that may point to a real incident. However, writing filters is a time-consuming task in itself. To create a correct filter, an analyst needs to: Review the logs Identify the source of the activity Define filtering boundaries Choose filtering criteria Write a rule Check that the filter is robust This post looks at how filter creation itself can be automated to reduce analyst workload. Filter validity criteria In the context of security alerts, a filter is a combination of event field values that marks a detection as a false positive and not worth the analyst’s attention. Filters can be generated for any combination of event fields, but not every combination yields a valid filter. That’s why we need to define validity criteria: conditions showing that a specific filter actually removes noise and doesn’t suppress useful alerts. Detection rules in security systems typically contain a set of conditions, and each of them can generate an alert. A specific event triggers a given fragment of a detection rule. The set of events that a given fragment responds to is what we’ll call its context. A good filter should cover the entire context where possible, without going beyond it. Filters that are too broad or too narrow are useless. For example, a filter based solely on PowerShell would exclude too many events, including some that deserve the analyst’s attention. Most of the filtered events would also be unrelated to the rule in question. A filter on a specific ID, on the other hand, would cover only one event, which may never recur. Problems in filter generation We can run into a number of problems when creating filters. First, events often contain an excessive number of fields (around 400 on average), and most of them aren’t needed for filtering. At the same time, generating and ranking combinations of these fields is also fairly resource-intensive. So, the first step is to discard the irrelevant fields and keep only those worth using in the filter. For example, out of 400 fields in an event, four may be enough for filtering. Another problem is string normalization. Field values (for example, the path to the same application on different systems) can vary depending on system settings, user folder names, and so on. For a filter to account for all these variations, the strings must be normalized. For file paths, in particular, you can replace the drive letter and user folders with standard environment variables and convert all paths to a single case. Finally, if a filter completely mirrors the query of a detection rule, it effectively disables that rule. To fix this, remove the event fields used in the detection rule from the filter. For example, if a rule triggers on a file path, there are two ways to filter out unwanted alerts: In the filter, use fields other than the file path. Use a part of the file path that the rule doesn’t use. For example, for the rule file_path: ".../something.exe", you can set up a filter that excludes events with the user/unique_files/ directory in the file path. Filtering security alerts with an algorithm Given the problems described above, automatically generating filters for security alerts should consist of the following steps: An analyst receives a security alert that they assess as a false positive and uses the filter generator. The generator takes the 400-field event from the alert, along with the rule that triggered on it, and determines the context. The generator selects the fields that are relevant to the filter. The generator normalizes the strings. The generator builds different combinations of the selected fields. To do this, we applied the FP-Growth (frequent pattern growth) algorithm to the context determined earlier. FP Growth builds a tree of frequently occurring items (fields) from a dataset (in our case, the context) and discards items that aren’t frequent enough. This reduces the number of combinations to check, which lowers the cost of finding the most relevant ones. The generator ranks the resulting combinations and offers the analyst the top three to choose from. For example, consider an event described by the following JSON: After generating and ranking the combinations, we got three filters based on file paths: processcmdline:("nodejs\\node.exe" AND "*****\\appdata\\roaming\\npm\/node_modules\/pi-coding-agent\/dist\/cli.js") file_path:"*****/.pi\\agent\\bin\\fd.exe" processcmdline: "programfiles\\nodejs\\node.exe" The last filter turned out to be too broad, but the analyst marked the other two as valid. Pros and cons of the algorithmic approach The algorithmic approach to filter generation has several advantages. First, it’s simple and deterministic: the same alert always produces the same result. Second, it’s interpretable, meaning we can see why a given combination ranked near the top of the list and why a particular field didn’t make it into the list of candidate fields at all. Third, the stages of the process are independent of each other, so individual steps can be improved and tuned without the risk of breaking the algorithm elsewhere. Finally, it has no external dependencies or models, which makes it easy to deploy in any customer environment. However, it also has drawbacks. The first is its dependence on context. If there’s no context for some reason — for example, the rule’s first-ever trigger — a filter can’t be created. The second problem is string normalization. There’s a high chance of running into atypical cases that the normalization rules don’t cover. Often such cases can’t be described correctly at all, because they contradict each other. These problems can be solved with large language models (LLMs). When an LLM generates the filters, no context is needed, because the LLM doesn’t iterate over or rank combinations. The LLM also takes a more flexible approach to string normalization, including non-standard cases. That said, an LLM doesn’t have to be involved at every stage of the generation process. We decided to keep the algorithmic solution where it works well. For example, a simple algorithm selects suitable fields without errors, so we didn’t move this stage to the model. Filter generation with an LLM The LLM-based filter generation looks like this: The input is still an alert containing an event with 400 fields, plus the rule that triggered on that event. Then an algorithm selects the most suitable fields for the filter. Next, the LLM processes the input — the event, the selected fields, and the detection rule — and generates three filter options along with an explanation for them. The explanation helps the analyst understand why these particular filters were suggested and make an informed choice. Consider the following event in JSON format: The LLM suggested three filter options that all contain the same command line fragment: file_path: "\\WindowsPowerShell\\powershell.exe" AND filecmdline: "Get-VM -Name kafka\*" filecmdline: "Get-VM -Name kafka\*" filecmdline: "kafka*" As in the previous example, two of the three output filters were valid. The last one was too broad to be usable. The analyst chose the first filter with minor edits: file_path: "powershell.exe" AND filecmdline: "Get-VM -Name kafka\*" Pros and cons of generating filters with an LLM Using an LLM solved the problems of purely algorithmic filter generation: dependence on context and errors in string normalization. However, the single-request approach had its own drawbacks. First, it had no output validation. The LLM returned filters as-is, with no syntax check and no way to correct errors. Second, unlike in the algorithmic version, all filter generation tasks were handled by a single system prompt. As a result, a minor fix in one place could break the logic in another. Using multiple AI agents To address the problems of generating filters with a single model and a single system prompt, we tried distributing the tasks across different LLM agents. In this version, we brought in agents as early as the stage of selecting relevant fields. The chain includes four agents in total: Selector: selects the relevant fields. Generator: generates filters. Validator: evaluates the generated filters. If the filters are found to be invalid, it returns the task to the generator. If the selected fields are found to be irrelevant, it starts a new field selection cycle. If the filters pass validation, or the limit on regeneration attempts is reached, the valid filters (or the best ones available) are passed to the analyst. Commentator: analyzes the output filters and explains which field values were used and why. For the following JSON event, the agents produce three valid filters based on the file path. The filters differ in the additional parameters that support the detection: file_path:"sysvol\\domain\\scripts\\logon.bat" file_path:"sysvol\\domain\\scripts\\logon.bat" AND file_md5:"0x7..6E" autorun_entry: "Group Policy\\State" AND file_path: "SYSVOL\\domain\\scripts\\logon.bat" Pros and cons of the agent-based approach LLM agents give us a system that can validate and correct filters. Its modular architecture also lets us tune and improve different stages of generation independently. However, this approach makes more LLM calls than the previous ones, so the system is slower. It also depends on access to a model, which may not be available in the organization’s isolated environment. Evaluating the filter generation system To evaluate how well the filter generation system works, we use two main metrics: Approval rate. For each security alert, the system suggests three filters. If the analyst selects at least one of them, we count the generation as successful. For example, in the case below, the approval rate is 2/3. False positive reduction rate (FP drop). This metric shows how much a filter helps reduce the number of false positives. It can only be measured over time, because we need to compare the number of false positive alerts received during a set period before and after the filter is deployed. Below is a comparison of the three filter generation approaches by these metrics: As the results show, LLMs significantly improve the efficiency of filter generation. At the same time, the LLM agentless and agent-based approaches perform almost identically. So we chose the simpler and faster option: an LLM with a single system prompt. As a result, we cut false positives by 99%, increased the number of filters generated per day by 28%, and made analysts’ alert handling 17% more efficient. Takeaways Practice has shown that filter generation can be automated effectively. We’ve looked at several options for automatically generating filters for false-positive security alerts: a purely algorithmic approach, an LLM-based approach, and an approach built on a group of LLM agents. All of them can generate valid filters and make the analyst’s work much easier, but each has its own pros and cons. The algorithmic approach scored lower on the evaluation metrics, but in two-thirds of cases, it still produces filters that satisfy the analyst. It works especially well when there’s already a set of similar events to filter out, meaning enough context has accumulated. The algorithmic generator can also be deployed in any environment. Both standalone LLM and multi-agent LLM approaches show high accuracy, and their results differ only slightly, so the slower agent-based approach looks excessive at this stage. If an LLM is available, the best option appears to be a single model that handles several stages of the generation cycle.
Binance, the world's largest crypto exchange, has linked the theft of about $6.3 billion in cryptocurrency to a DevOps engineer's compromised laptop. The crypto exchange said the access let attackers submit unauthorized withdrawal transactions after they accessed a service container, and it hasn't yet found evidence of a leaked wallet private key. "To be clear: the attacker did not simply use one engineer's permissions, and this was not a single 'one-click' action. It involved a more complex attack process. Our investigation has not attributed responsibility to any individual," Binance Co-CEO Richard Teng said. Binance also said it "uncovered additional points of breach" but was "still gathering the facts, and will share more directly as we have them." The company confirmed the breach in a statement shared with BleepingComputer on Thursday and reassured customers that their funds were safe.
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