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SQLite WAL-Reset Bug
Many of these outages were caused by a single bug, deep in SQLite. It took months of intense forensics to track it down.
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This lack of reliable trigger conditions meant we couldn’t reproduce the bug synthetically. Instead, we had to rely on deploying passive, forensic telemetry in our live environment to catch the corruption red-handed. Gathering live diagnostics for a database issue is the last thing we wanted to do, but we had no choice.
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As an additional complication, the corruption didn’t occur on a regular schedule. Sometimes incidents would be hours apart, other times weeks. This made it difficult to predict progress or plan further work, because we were never sure when we’d get our next diagnostic dump. We had a six-week period between October and December when there were no corruption incidents, before they returned as an unwelcome Christmas present.
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In two incidents, our transaction logs failed to replay cleanly. Upon closer inspection, we discovered that data written and committed by one transaction was inexplicably invisible to later transactions. A write had vanished into thin air without raising an error.
The bug is likely present in all version of SQLite from 3.7.0 (2010-07-21) through 3.51.2 (2026-01-09). It is fixed in version 3.51.3 (2026-03-13) and later. Backports of the fix are available for some earlier releases: 3.44.6 and 3.50.7.
The bug only affects databases in WAL mode when there are two or more database connections open on the same file, in separate threads or processes, and when those two connections attempt to write or checkpoint at the same instant.
This investigation is a useful reminder: running boring technology in a non-standard way is a risk. The common paths and standard configurations are incredibly well-tested and reliable. Most people use SQLite in a standard configuration and never face this sort of issue. Everything we were doing was a public, documented, supported configuration—but by taking manual control of the checkpointing process and running at our own aggressive pace, we stepped off the well-trodden operational path.
I spent the last two years of my time working on SQLite trying to chase this bug down. It was so rare that I never got an internal report; my assessment of the number of customers affected was “about a county’s worth”, some of whom were generous enough to allow their local Genius Bar to send me backups of the affected databases. I spent so many hours in a hex editor staring at the wreckage.
Top three data corruption bug of my career for sure, and the other two weren’t bugs in SQLite.
A former Tailscale employee got in touch and shared that they were checkpointing these databases every 250ms. Multiplied across a fleet of servers that is a fantastic way to surface a super rare bug—they basically built our test rack as a product, but two orders of magnitude more effective (partially due to scale, but mostly because our rack also panics devices at random in order to test the filesystem, NAND controller, etc. and rebooting takes a while)
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13/10 fantastic bug. proof that 100% test coverage is a good start, and that formal methods are needed for critical applications.
I whipped out my phone, and asked Claude to get to work. I had it get SQL 3.51.2 – still buggy – set up in Antithesis, and then instrument the code with a bunch of Antithesis assertions. You can see the instrumented version here.
Then I asked it to write a simple workload which exercised the WAL insert and checkpoint code. Notably, this is a completely generic workload. It just runs writes and checkpoints concurrently – things you’d expect to actually happen in production, all the time. The assertions are also generic to the bug, they’re all standard assertions you’d add to any database, things like “no lost committed writes” and “database is not corrupt” (called integrity check in sqlite).
On my first run, Antithesis caught the bug in 15 mins. Here’s the report.
Of course, it helps that he knew to look in the checkpoint code, but you could imagine using tools like this to quickly investigate hypotheses when you don’t already know the answer.
Oracle simplifies migrating legacy databases off IBM mainframes with support for EBCDIC
Oracle on Tuesday described new EBCDIC character set compatibility features in Oracle AI Database for customers transitioning from legacy databases on IBM mainframes.
In its post, Oracle noted that EBCDIC compatibility has historically been one of the top technical challenges for enterprises re-platforming to use newer, ASCII-based databases while continuing to use proven legacy applications.
“Achieving this goal requires more than simply moving data. It requires preserving the EBCDIC compatibility on which existing applications depend,” wrote Michael Yau, VP for Oracle Database Globalization Engineering. The feature rollout “addresses two fundamental challenges of preserving EBCDIC compatibility: accurate character encoding conversion and preservation of EBCDIC binary ordering.”
He added: “These client character sets implement IBM Character Data Representation Architecture (CDRA) code page definitions, providing source-to-target character mappings that are compatible with IBM’s published standards. This enables accurate and predictable character encoding conversion during data migration and subsequent database client/server communication.”
Yao observed that this is important because these mainframe migrations can be very complex.
“Many legacy EBCDIC applications, such as those written in COBOL, implicitly rely on the EBCDIC binary ordering defined by IBM EBCDIC code pages. SQL predicates that compare character values, perform range searches, or sort query results often assume this ordering,” he wrote. “After migration to an ASCII-based Oracle AI Database character set, these same SQL statements can produce different results, not because the data changed, but because the database’s default binary ordering follows that of the ASCII-based database character set rather than the source EBCDIC code page.”
Compatibility repair, not modernization
While consultants generally applauded the new features, some questioned whether this will simply shift enterprise dependency on IBM to dependency on Oracle.
Sanchit Vir Gogia, chief analyst at Greyhound Research, is one of the fans.
“Oracle has repaired one of the oldest silent faults in mainframe migration: EBCDIC ordering, the muscle memory of the legacy estate. Preserve the data and lose the ordering, and a query returns the wrong record while every dashboard stays green,” he said. “The application runs and the query completes. The answer is simply wrong.”
Gogia noted that the new feature is “not a modernization suite. It is a compatibility repair, narrow and genuinely useful, which CIOs who have bled on past migrations will read with equal parts relief and suspicion.”
AJ Thompson, CCO at UK IT consulting firm Northdoor, agreed that the Oracle announcement addresses a genuine technical barrier rather than just being a marketing gimmick, so it is worth taking seriously as a re-platforming enabler. “The two problems it solves, EBCDIC to ASCII character conversion and preserving EBCDIC binary sort ordering, have historically been real blockers for allowing COBOL to move away,” he said.
But, he cautioned, CIOs must also take resiliency challenges seriously. “Mainframes are not chosen primarily for character encoding, they are chosen for benefits like decades of proven uptime, IBM Z’s redundancy architecture, and workload isolation,” he pointed out.
“Oracle’s announcement solves a data compatibility problem, not a resilience or availability one. A client with genuinely mission-critical, zero-downtime workloads will still need convincing on the availability and disaster recovery side before moving [to the cloud], and Oracle’s own resilience claims would need scrutiny on their own merits, quite separate from this EBCDIC work.”
Leverages IT desperation
Mike Wilkes, enterprise CISO at Aikido Security, added that Oracle is leveraging IT desperation to squeeze long-term value from legacy systems.
“I have always believed that Oracle will own the very last white-knuckle-grip workloads that migrate from on-premises data centers into the cloud,” he said. “This announcement certainly demonstrates that they understand their position in the world of cloud service providers. They are not the biggest, they are not the oldest, and they are not the most technically advanced. But they do own the market share for the trailing edge of cloud adoption.”
He pointed out that the greatest barrier to cloud migration is not containerizing modern applications, it is the decades of business logic buried inside COBOL applications and EBCDIC-encoded data.
“Oracle’s EBCDIC compatibility features acknowledge a practical reality: organizations are not rewriting these systems from scratch,” he said. “If Oracle can reduce the cost, risk, and operational disruption associated with moving those workloads, the announcement represents meaningful value for IT teams that have been delaying modernization because the migration path was simply too complex or too expensive.”
Could cause vendor lock-in
Then again, Wilkes noted, there is the potential for increasing vendor lock-in.
“Compatibility layers almost always increase long-term dependence on the platform providing them,” he said. “Rather than eliminating legacy technology, they abstract it behind Oracle’s database and cloud ecosystem, making future migrations potentially more difficult. Enterprises should view these tools as transition accelerators rather than permanent architecture.”
Thus, he said, if they use the opportunity to gradually modernize applications and data models, there is substantial value, but if they simply relocate technical debt into Oracle Cloud, they may find that they have exchanged one form of legacy lock-in for another.
Easier, but not easy
Matt Kimball, VP and principal analyst with Moor Insights & Strategy, also saw the Oracle move as a good one, but stressed that while it should make things easier for organizations, re-platforming still won’t be easy because EBCDIC migration isn’t just a character-conversion exercise. However, “Oracle’s built-in character-set support and EBCDIC collations move part of that compatibility burden into the database, making it easier to preserve existing application behavior,” he said.
Ishraq Khan, CEO of coding productivity tool vendor Kodezi, agreed.
“One of the biggest obstacles to leaving mainframes is decades of applications built around EBCDIC encoding and legacy data formats. If these compatibility features reduce the amount of code that needs to be rewritten, they can lower migration risk, cost, and implementation time,” Khan said. But, he added, organizations should also consider whether these features simply make migration easier, or make future moves more difficult.
This article originally appeared on CIO.com.
Oracle’s July update fixes ten 10.0 vulnerabilities in Fusion Middleware
Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.
Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).
These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,
No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.
Two critical flaws in Oracle Database Server
The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.
This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.
The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.
Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”
Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.
“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.
A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.
CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.
Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.
There were also two critical flaws in Oracle’s TimesTen in-memory database.
The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.
Volume repair
Gogia said the volume itself marks a shift.
“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”
He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”
Third Tuesday, quarterly cycle
The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.
Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.
“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”
Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”
Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.
“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.
Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.
Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.
This article first appeared on CSO.
New AI database reduces hallucinations by 78% and delivers answers 20 times faster
Artificial intelligence is becoming increasingly important in businesses, helping employees search through large amounts of documents and answer complex questions. However, one major problem still limits the use of AI in many industries: hallucinations. An AI hallucination happens when a system generates information that sounds convincing but is actually wrong. This can be a serious […]
The post New AI database reduces hallucinations by 78% and delivers answers 20 times faster appeared first on Knowridge Science Report.
Retinal Imaging Reveals Key Predictors of Alzheimer’s Disease Risk
Often hailed as “the window to the soul,” the human eye has long fascinated scientists and philosophers alike. However, beyond its poetic allure, emerging research suggests that the eyes provide a vital window into the health of the brain. A groundbreaking new study spearheaded by the University of Florida’s biomedical engineering department has unveiled a transformative approach to predicting Alzheimer’s disease risk factors by analyzing retinal photographs with cutting-edge deep learning algorithms. This development promises to revolutionize early detection and intervention for one of the most devastating neurodegenerative diseases.
Traditionally, diagnosing Alzheimer’s disease has been a challenge due to its insidious onset and prolonged development over decades. Most diagnostic protocols focus on detecting the disease in its later stages, by which time significant, irreversible brain damage has already occurred. Recognizing this limitation, Dr. Ruogu Fang and her interdisciplinary team leveraged advances in artificial intelligence and biomedicine to explore the retina—a part of the central nervous system accessible via non-invasive imaging—as a potential biosensor for Alzheimer’s risk.
The study capitalized on a rich database of over 40,000 retinal images collected from the UK Biobank, a comprehensive health resource with longitudinal patient data. Retinal photography is commonly conducted during routine eye exams, diabetes management, glaucoma monitoring, and cataract assessments. By harnessing these widely available, inexpensive images, the research team circumvented the need for costly, invasive, or logistically complex diagnostic tools such as MRI or PET scans, thus holding promise for scalable, population-wide screening.
Employing deep learning, a subset of machine learning characterized by neural networks modeled after the human brain, the researchers developed an AI framework that scrutinized retinal morphology. This model was able to discern subtle variations in retinal arteries, veins, and the optic nerve head, features imperceptible to the human eye but correlated strongly with known risk factors for Alzheimer’s disease. Such morphological markers serve as proxies for neurovascular health, which is crucial given Alzheimer’s recognized association with vascular impairment.
One of the most striking revelations of this research was the model’s capacity not only to predict intrinsic biological traits such as sex and blood pressure but also modifiable lifestyle factors that influence Alzheimer’s risk. The AI effectively inferred behaviors like smoking, alcohol consumption, and even insomnia from retinal images alone. This objective measurement circumvents the notorious unreliability of self-reported lifestyle data, often plagued by bias and inaccuracies, thereby enriching risk assessment precision.
As Dr. Fang points out, retinal morphology functions less like a mere clinical questionnaire and more like an integrative biological sensor reflecting a cumulative lifetime burden of neurovascular and lifestyle insults. This conceptual shift underscores the retina’s role in encapsulating decades of physiological data, transforming retinal imaging into a powerful tool for capturing both present health and future vulnerability.
Furthermore, the implications extend beyond risk prediction. Identifying individuals with early signs of retinal changes could trigger timely clinical interventions long before cognitive symptoms manifest. Such preclinical detection enables the implementation of protective lifestyle adjustments, pharmacological therapies, or cognitive training programs designed to delay or mitigate Alzheimer’s progression.
The pioneering work built on previous findings from Fang’s group that demonstrated retinal images’ utility in detecting active Alzheimer’s disease cases. However, this new model pioneers the field by focusing on prodromal markers and risk factors instead of established dementia, bridging a critical gap in neurodegenerative diagnostics.
Importantly, the use of retinal photographs democratizes neurovascular health monitoring. Since retinal imaging is non-invasive, accessible, and affordable, it offers the prospect of integrating brain health assessment into routine eye care, thus expanding preventive neurology beyond specialized clinical settings.
This research also exemplifies the synergy between computational science and biomedical engineering. By combining sophisticated image analysis algorithms with large-scale patient data, it transcends traditional diagnostic constraints, marrying technology with medicine in a profoundly impactful manner.
While challenges remain, including the need for further validation across diverse populations and integration with other biomarkers, this study sets a new paradigm for early Alzheimer’s disease risk stratification. The possibility of preemptive interventions guided by retinal AI analysis heralds a future where the devastating trajectory of Alzheimer’s may be altered through timely and personalized healthcare.
In summary, the novel application of deep learning to retinal photographs opens an unprecedented window into brain health, heralding a new era of precision neurodiagnostics. It moves us closer to the long-sought goal of identifying Alzheimer’s risk decades in advance, fostering opportunities for early intervention and ultimately, better patient outcomes.
Subject of Research: People
Article Title: Prediction of Alzheimer’s disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank
News Publication Date: 16-Jun-2026
Web References:
DOI: 10.1177/13872877261457650
Keywords
Alzheimer’s disease, Neurodegenerative diseases, Neurological disorders, Eye, Retina, Artificial intelligence, Image analysis, Deep learning, Biomedical engineering, Neurovascular integrity, Retinal biomarkers, Predictive modeling
