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.
Drupal has warned that threat actors are exploiting a "highly critical" security flaw in its Commerce Core component. The vulnerability, tracked as CVE-2026-118539 (CVSS score: N/A), is a case of missing authorization affecting Commerce Core versions from 0.0.0 to 2.41 and from 3.0.0 to 3.3.13. "This module enables you to sell goods and services to customers through an included shopping cart
Cryptocurrency giant Binance said on Thursday that a "security incident" on its platform has impacted about $6.3 billion worth of assets. The world's biggest cryptocurrency exchange said nearly $3 billion of those assets have been frozen on-chain so far. Tether and Circle said they have frozen more than 1.567 billion USDT and 800 million USDC related to the incident, respectively. The
OpenAI says it won't reduce usage limits for the $100, $200, and $500 ChatGPT Pro plans, as well as Business Premium, and it also promised to keep Dots usage independent. In a post on X, Tibo, who is the face of ChatGPT and Codex on X, confirmed that the changes take effect immediately and primarily affect ChatGPT Work and Codex, which share usage allowances under eligible subscriptions. OpenAI had previously planned to reduce the included usage allowance for existing Pro 200 subscribers after October 29, despite keeping the subscription price at $200 per month. However, OpenAI has now reversed that decision following customer feedback. "We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today," Tibo announced on X. "We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same."
Cryptocurrency exchange Binance is investigating a potential security breach involving up to $5.8 billion in digital assets, in what could become the largest cryptocurrency hack in history. If confirmed stolen, the amount would far exceed the $1.5 billion stolen from Bybit by North Korean hackers in February 2025, the largest confirmed crypto heist on record. The suspected breach has already prompted stablecoin issuers Tether and Circle to freeze more than $2.3 billion in assets associated with the incident. In a statement to BleepingComputer, a Binance spokesperson confirmed that the exchange was investigating reports of a potential security issue while reassuring customers that their funds were safe. "Our teams are aware of reports of a potential security issue and are actively investigating. Rest assured that user funds are completely safe," the spokesperson told BleepingComputer.
A crypto-mining campaign abusing Hugging Face models returned days after takedown, hijacking GPU servers and planting backdoors, with one model downloaded over 28,000 times. Separately, a fake Solana starter kit on GitHub is positioned to trick AI coding agents into installing a NetSupport RAT.
Japan Airlines (JAL) has announced that a data breach at Amadeus’s Passenger Service System exposed the data of about 92,000 customers. JAL Group, which operates the airlines JAL and Japan Transocean Air, uses the system to handle passenger reservations, ticketing, and check-in. According to its announcement, Amadeus notified the airline on October 23, 2026, that unauthorized access to its systems occurred between August 10, 2023, and September 23, 2026. As a result of this lengthy breach, the threat actor accessed reservation codes (PNRs), names, flight dates, routes, and travel classes for approximately 90,000 customers. An additional 2,000 customers had the above data types exposed, plus their email addresses, phone numbers, passport numbers, nationalities, frequent flyer numbers, and flight, ticket, and seat numbers.
42 publicly listable cloud buckets containing 18 billion files, including exposed credentials, database dumps and backups. The Ransomnews research team counted 484,142 publicly listable cloud storage buckets in October 2026, holding 18.05 billion files. They built the count from the search engines that index open cloud storage and published it as the Ransomnews Cloud Exposure Index, written by Mantas Sasnauskas. “No file was opened for this index. Every number below comes from bucket names, file extensions, file counts and last-modified timestamps. That is a deliberate limit. It keeps the research on the right side of the line, and it turns out to be enough to see where the sensitive material concentrates.” Ransomnews states. “We are calling it the Ransomnews Cloud Exposure Index, and we will rerun it quarterly so the numbers can be compared over time.” The researchers used bucket names, file types, file counts and last-modified dates to collect their data. They did not open any files, allowing them to identify where sensitive information might be stored without accessing the content. They plan to repeat the research every three months to track changes over time. Amazon S3 accounts for 72% of the exposed buckets, roughly in line with its share of the market. This does not mean S3 has weaker security than other providers. Azure Blob, however, appears 2.5 to 3.6 times more often than expected among buckets named for backups, data exports and payroll records. The researchers also found 321,058 files related to credentials and keys. Of the exposed .env files, 67% were last modified in 2026. Below is the split by provider, from a snapshot taken on October 7: ProviderOpen bucketsShareAmazon S3347,98471.9%Azure Blob68,53214.2%Google Cloud Storage53,13111.0%DigitalOcean Spaces11,0692.3%Alibaba OSS3,4260.7%Total484,142100% A lot of these buckets are public on purpose, holding website assets, CDN mirrors, open datasets and podcast archives. So the index isn’t 484,142 breaches. It’s the pool where breaches hide, and the rest of the report narrows it down to the stores that should never have been listable. “Azure, by a wide margin relative to its size. Azure Blob holds 14.2% of open buckets, but 37% of the 1,459 buckets with “backup” in the name, 42% of the 399 named for exports, and 51% of the 505 carrying HR or payroll terms.” continues the report. “Buckets named for logs, customers and databases sit at 35% to 36%, all around two and a half times Azure’s baseline.” The skew only shows up for operational data. Buckets named “dev” sit at Azure’s baseline of 15 percent, and “test” and “staging” fall below it, at 9 and 7 percent. The authors suspect Azure storage accounts more often hold line-of-business data such as backups, exports and HR systems that enterprises run on Microsoft, while S3 and Google Cloud carry more application assets. They call it a hypothesis the counts suggest and don’t prove, and it’s the first thing they want to test next time. By sector, retail and e-commerce names lead with 5,666 buckets, mostly storefront assets. The more worrying number is that names tied to regulated sectors (finance, healthcare, education, government, legal and HR) appear on 10,891 unique open buckets. Those are the places where a stray file most likely holds personal, health or financial records, and where regulators expect to hear about it. Sector (by bucket name)Open bucketsFiles in themRetail and e-commerce5,666269.9MFinance and insurance3,50858.0MHealthcare2,70947.9MMedia and entertainment2,696136.2MEducation2,39360.6MGaming2,29679.0MTravel and hospitality1,714118.7MReal estate1,13760.7MGovernment and public sector1,00036.2MLegal97811.3MCrypto63011.9MHR and payroll5058.4M Healthcare-related names appear in 2,709 buckets containing 47.9 million files. The researchers identified them using terms such as “hospital” (135 buckets), “patient” (135), “clinic” (277) and “dental” (331). HR and payroll is the smallest sector, with 505 buckets, 259 of which are hosted on Azure. These figures are based on names, not proof of ownership. The search matches parts of words, so “store” can refer to object storage as well as shops. A single bucket can also appear in two sectors. The exposed buckets also list about 4.7 million database and dump files, 15,130 mailbox archives, and tens of millions of spreadsheets and documents. Credential files are particularly concerning because they can turn a cloud misconfiguration into a real intrusion. File typeWhat it isFiles listed.pemCertificates and private keys113,285.envApp secrets and API keys80,495.p12Certificate bundles with keys33,268.keystoreJava and Android keystores27,229.ppkPuTTY SSH private keys24,302.pfxWindows certificate bundles23,352.ovpnOpenVPN client configs15,079.jksJava keystores2,421.htpasswdWeb server password files695.tfstateTerraform infrastructure state475.kdbxKeePass password vaults457 Each of these exists to grant access. A .env file is where developers keep database passwords and API keys. A .tfstate file is Terraform’s record of a whole cloud setup, often with secrets in plain text, and a .kdbx vault is encrypted but now open to anyone who wants to guess passwords offline. Some .pem files are public certificates and some files are test fixtures, so the counts are a ceiling on live secrets and not a measure of them. It’s still a big ceiling. Database files make up most of the sensitive material by volume: File typeWhat it usually holdsFiles listed.sqliteEmbedded app databases2,561,807.bakDatabase and server backups752,055.sqlDatabase dumps687,446.sqlite3Embedded app databases488,176.dumpDatabase dumps176,603.mdb / .accdbMicrosoft Access databases36,467.dmpMemory and crash dumps12,809.pst / .ost / .mboxMailbox archives15,130.emlSaved emails4,549,665.csvData exports and recordsat least 69,580,112.xlsx / .xlsSpreadsheets42,544,997.docxWord documents24,936,788.pdfDocuments, invoices, scansat least 106,191,393.logApplication and server logsat least 61,310,809 The exposed buckets contain thousands of virtual disk images, including VMware, Hyper-V and QEMU files. A single disk image can contain an entire server, including credentials. Some exposures involve active systems. Of 80,495 .env files, 67% were last modified in 2026. Certificates and SQLite databases include both recent and older files. Backups appear older, with 89% of the 752,055 .bak files last modified before 2025. Almost all PuTTY key files date from 2024. Recent leaks need urgent attention, while long-standing exposures point to poor security management. The problem is ongoing: at least 113.6 million files were added to publicly listable buckets in the seven days before September 9. Over the previous 30 days, these included more than 21,000 SQL dumps, nearly 21,000 backup files, 4,477 .pem files and 1,298 .env files. Public .env files can expose database passwords that ransomware affiliates and initial access brokers look for. Similar credentials appear in stolen-data logs, but in this case, attackers do not need to infect a device. They only need to search for exposed files. Cloud providers already offer settings to prevent public access. Organizations should enable S3 Block Public Access, disable anonymous access to Azure Blob storage, and enable public access prevention in Google Cloud. These controls should apply at account or organization level to prevent new storage buckets from becoming public. Organizations should also scan their storage for sensitive files such as .env, .sql, .bak, .pem, .ppk, .tfstate and .kdbx. Terraform state files and virtual disk images should be treated as secrets, stored privately, encrypted and monitored. Backups should never be publicly accessible, and recovery procedures must be tested. One exposed bucket may be a mistake, but many often indicate a wider process failure. Organizations need to fix the underlying security controls, not just individual buckets. The research has some limitations. The team queried search engines on October 25, while the bucket list was current to October 7 and the newest indexed file dated September 9. File-level data may therefore be about a month old. Counts are also capped at one million per query, so researchers split larger categories into date ranges and added the results. Some limits apply. The team queried the search engines on October 25. The bucket list was current to October 7 and the newest indexed file was dated September 9, so file-level figures lag by about a month. The index caps any single count at one million, so larger types were split into date windows and summed. A last-modified date shows when a file was written, not when it was exposed, and a listed file isn’t proof that its contents can be read. No bucket is named in the report. Exposures with a known owner in regulated sectors are going through responsible disclosure, with national CERTs copied where the owner is public sector or doesn’t respond. “Rotate any secret that has ever sat in a public bucket. Closing the bucket does not un-leak the key. Assume anything that was listable was copied.” conlcudes the report. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Cloud Buckets)
A newly identified advanced persistent threat (APT) group tracked as UAT-11473 was observed exploiting CVE-2026-20127, an authentication-bypass flaw in Cisco Catalyst SD-WAN Controllers, to deploy a custom malware framework called KeyDrift. CVE-2026-20127 is a max-severity flaw that Cisco patched in February 2026, which allows remote attackers to gain privileged access to Catalyst SD-WAN Controller and Manager and add rogue peers. UAT-8616 has exploited the vulnerability as a zero-day since at least 2023, and recent research also linked its exploitation to the Chinese Volt Typhoon state-sponsored hacking group. According to Cisco Talos researchers who reported on the activity, the UAT-11473 threat cluster appears to overlap with Volt Typhoon’s "Silent Train" covert access network, but they made no definitive links.
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