找到 476 篇相关文章
“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman” systems, while a separate faction, led by policy organizations…
InfoQ has opened enrollment for the InfoQ Certified AI-Assisted Engineering Program, a five-week online certification program for senior engineers and architects who already run a coding agent against production code daily, where the open questions have moved past prompting into what the agent is allowed to touch and what catches its mistakes before a human does. By Artenisa Chatziou
Artificial intelligence (AI) makes all code write-only,. It’s too dense to read, and tests define the behaviour and become the documentation. Code is also disposable; it becomes easier to rewrite than to debug. Humans can't review AI-generated code at scale. Intent decouples from implementation; developers should focus on creativity. By Ben Linders
Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, season, time of day, current events, global markets, and competitor airline activity to name…
Introducing Intelligence Age, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom.
A year ago, the debate between “vibe coding” and “prompt-driven development” felt like the right lens for using AI in software. Today it’s outdated. The real frontier is no longer how to phrase a request, but how to build an environment that lets an AI agent explore, modify, and verify code safely. The shift from conversational prompts to structured development harnesses changes what it means to ship reliable software. Below are four concrete ways this evolution is reshaping daily work. From Prompt to Harness A finely crafted prompt can still produce a working feature, but it won’t prevent an agent from inventing APIs, skipping tests, or misreading business rules. The new bottleneck is the harness: Git for traceability, tests for feedback, linting for style, and explicit rules for constraints. When these elements are in place, even a short instruction like “fix the bug” can trigger a reliable, auditable workflow. Code is Abundant; Good Software is Rare Generating hundreds of lines of code costs almost nothing, but verifying that those lines align with product history, security policies, and hidden constraints is still manual. The developer’s scarcest resource is now judgment: deciding what to automate, what to document, and when to override the agent’s plausible but wrong solution. Documentation is Now for Machines README files and ADRs used to serve only humans. Today they also feed the agent’s context. A well-maintained AGENTS.md , coherent tests, and executable examples let an AI act with minimal prompting while remaining aligned with the team’s conventions. The repository becomes a self-explaining environment. Supervision is the Next Skill Single-agent workflows are giving way to multi-agent orchestration. Frontend, API, test analysis, and incident triage agents must coordinate without stepping on each other. Observability, conflict detection, and rollback mechanisms replace prompt tuning as the critical layer of control. The center of gravity has moved from “ho
During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool.
Anthropic announced last week it would include invisible watermarks in AI-generated content to comply with new EU rules. Within hours, overrides were being touted online.
Flock’s surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates.
Forminator Forms (CVE-2026-15748): Unauthenticated RCE via Forged Upload Settings in Select Fields 1. Basic Information Article Title : 600,000 WordPress Sites Affected by Arbitrary File Upload Vulnerability in Forminator Forms WordPress Plugin Publisher : Wordfence Intelligence Publication Date : 2026-08-17 Severity : High Original Source : Wordfence Related Sources : SecurityWeek Related Malware / Attack Groups : None / Unidentified CVE & Product : CVE-2026-15748, WordPress, Forminator Forms before 1.56.1 2. Summary An attacker can inject fake upload settings into a public form's Select field and bypass PHP blocks using strings like ph(p)|text/x-php . This uploads a file to the server. If the custom upload directory lacks execution prevention, it becomes an unauthenticated Remote Code Execution (RCE) vulnerability. 3. Attack Flow An attacker finds a public Forminator form that has both a File Upload field and a Select field. The attacker injects a fake record into the nested value of the Select field. This record includes return , field_type=upload , a custom name , and field_array . The function set_field_data() adds this fake record to the internal field_data_array . Because a real File Upload field exists, process_uploads() runs, and it trusts the fake record as a valid upload setting. The attacker controls the custom file type and additional-type . To bypass exact-match blocks for forbidden extensions, the attacker uses ph(p)|text/x-php . Because it is not an exact match for the key php , it passes the blocklist. However, the WordPress matcher matches ph(p) to .php . The server saves the PHP file. If the custom File Upload storage root lacks execution prevention (such as an .htaccess file), the attacker accesses the saved URL to run the PHP code. Inference : The attacker installs a web shell, steals WordPress settings, databases, or credentials, and defaces the site. 4. Attacker Position and Execution Location The attacker is external and can access public Wor
Clop's Windchill Web Shell: From Credential Decryption to Design Data Theft Inside the App 1. Basic Information Article Title : Clop Returns with Custom Implant in Mass-Extortion Campaign Publisher : ReliaQuest Threat Research Team Publication Date : 2026-08-18 Severity : Critical Original Source : ReliaQuest Related Sources : BleepingComputer , Ransom-ISAC Related Malware & Threat Groups : Clop / Cl0p, Windchill-specific JSP Web Shell CVE & Products : CVE-2026-12569, PTC Windchill, PTC FlexPLM 2. Summary This attack involves Clop exploiting a Windchill vulnerability to deploy a custom web shell. The web shell uses the application's own permissions and internal APIs to decrypt credentials in plain text, list design file locations, and steal the data. 3. Attack Flow Confirmed Core Chain Exploit CVE-2026-12569 on unpatched, internet-facing Windchill or FlexPLM servers. Place a custom JSP web shell inside the Windchill codebase. The attacker sends commands using the HTTP header X-windchill-req . The S command reads configuration files and uses WTKeyStoreUtil.decryptProperty() to decrypt saved secrets, such as LDAP administrator credentials. The L command queries Windchill database tables ( ApplicationData , FVITEM , FVMOUNT , MasteredOnReplicaItem , etc.) and writes file IDs, names, paths, and sizes to flst.txt . The G or D command reads the selected files. The server compresses the response with GZIP and returns it to exfiltrate the data. Optionally, the J command receives a Base64-encoded ZIP file to load and execute additional Java bytecode directly inside the Windchill process memory. Potential Subsequent Compromise Facts : The class loader can execute additional Java code in memory. Inference : Decrypted LDAP or admin credentials can be used to authenticate to other systems and move laterally. Hypothesis : Attackers may deploy ransomware or long-term persistence modules against organizations that refuse to pay. 4. Attacker Position and Execution Location The initi
The ChatGPT maker says its upcoming Astra model may have reached “critical” cyber capabilities, prompting it to halt a significant number of training runs while it tightens internal safeguards.
A video in a MacOS Tahoe release candidate version shows a user wearing AirPods, looking at a book, and talking to Siri.
AI companies like Anthropic and OpenAI regularly publish reports on how people are using products like Claude and ChatGPT, but they only release the data they want us to see, AI researchers say. “There is no independent source to corroborate it,” says Anka Reuel, a Computer Science PhD candidate at the Stanford Trustworthy AI Research…
The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight. LLMs can already write code, generate synthetic data for training, and optimize the computer chips they run on. Forecasts of explosive AI progress predict that what researchers call recursive self-improvement is on the horizon. …
Z.ai’s latest AI model release could help companies secure their systems—or find its way into the hands of hackers.
F-RevoCRM CVE-2026-71368: Cross-Site Scripting Targeting Logged-in Users 1. Basic Information Article Title : Cross-Site Scripting Vulnerability in F-RevoCRM Publisher : JVN Published / Updated Date : 2026-08-17 Severity : Medium Original Source : JVN#58692577 Related Information : F-RevoCRM Developer Advisory Related Malware / Threat Groups : None / Unidentified CVE & Products : CVE-2026-71368, F-RevoCRM 7.3.0 to 8.0.3, Fixed version 8.0.4 2. Summary This is a Cross-Site Scripting (XSS) vulnerability. If an attacker lures a logged-in F-RevoCRM user to a crafted URL, arbitrary scripts can run in the CRM's origin. This can lead to the theft of session information or unintended CRM operations using the user's privileges. 3. Attack Flow Chain Using a Crafted URL An attacker prepares a crafted URL or web page targeting vulnerable F-RevoCRM. The attacker lures a logged-in user to the URL via email, chat, or a website. The user's browser sends a request to the affected version of F-RevoCRM. The attacker's arbitrary script runs in the F-RevoCRM origin. The script tries to perform CRM operations, read screen data, or steal session information in the user's session. The attacker may use the stolen information or the existing session to abuse the account. Public documents do not confirm the exact type of XSS, the vulnerable parameters/endpoints, or whether cookies can be retrieved. Therefore, we do not conclude this is "Reflected XSS" or a "successful cookie theft." 4. Attacker Position and Execution Location The attacker can prepare the crafted URL or page without authentication. User interaction and a valid F-RevoCRM login session are required. Malicious JavaScript runs inside the user's browser with F-RevoCRM origin privileges. Subsequent malicious operations may reach the F-RevoCRM server as legitimate sessions. 5. Visibility for Victims and Administrators Victims / Users The CRM screen may still look normal after opening the URL. Screen transitions, unintended updates, r
Microsoft's AI Defense Research: Generating Detection Test Logs from Attack Procedures 1. Basic Information Article Title : Accelerating detection engineering using AI-assisted synthetic attack logs generation Publisher : Microsoft Security Blog / Microsoft Defender Security Research Team Publication Date : May 12, 2026 Importance : High (Practical value for defense research and testing environments) Original Source : Microsoft Security Blog Related Sources : MITRE ATT&CK / OTRF Security Datasets / ATLASv2 Paper Target : Attack logs used for designing and testing detection rules Main Methods : Prompt-based generation, multi-AI collaboration, LLM-as-a-Judge, reinforcement learning with verifiable rewards 2. Summary in One Sentence This research takes MITRE ATT&CK attack techniques and specific attack steps, feeds them to an AI, and creates detection test logs that include process names, parent processes, and command lines. In experiments, a method where multiple AIs share the roles of generation, review, and correction worked best, but synthetic logs are not proof of real-world attack reproduction and are limited to supporting lab tests. Terms Used in This Report Synthetic log : An artificially created log made by AI or other tools, not recorded by a real machine TTP : Information showing what an attacker wants to achieve, what techniques they use, and how they execute them Ground truth : Logs obtained from real attack reproductions used as a comparison standard Recall : How well synthetic logs include important events found in ground truth logs LLM-as-a-Judge : A method where another AI evaluates the validity of generated results 3. Defense Challenges to Solve Real attack logs happen rarely, making it hard to collect enough volume Collecting attack logs, labeling them, and reconstructing attack scenarios take time and money Reproducing real attacks in a lab is accurate, but trying many techniques in a short time is difficult Real logs from customer environments cont
CrowdStrike's AI Triage Research: How Well Can AI Automatically Judge SOC Alerts? 1. Basic Information Article Title : Teaching AI to Reason Through Detection Triage Publisher : CrowdStrike Publication Date : 2026-08-17 Severity/Importance : High (Practical value as defense research) Original Source : CrowdStrike Related Source : Research Paper (arXiv PDF) Target : Security alerts generated on Windows endpoints Main Models & Methods : Nemotron-3-Nano-30B, Nemotron-3-Super-120B, GEPA, AdaSTaR, LoRA, GRPO 2. Summary in One Sentence This is research on having AI judge whether Windows endpoint alerts are "real attacks" or "harmless false positives." While it showed certain results, misjudgments increased as time passed, revealing that continuous accuracy checks are necessary to automatically close alerts using AI alone. Terms Used in This Report True Positive : An alert that was genuinely an attack False Positive : An alert that was actually not a problem Precision : The rate of items judged by the AI as a specific classification that were actually correct Recall : The rate of alerts that the AI successfully caught out of all alerts that should belong to that classification Confidence : How certain the AI is about its own judgment Calibration : Adjusting and checking whether the AI's confidence matches its actual correctness Distribution Shift : Changes in operational alerts compared to training data due to new attacks or product updates 3. Defense Challenges to Solve SOCs receive a huge volume of alerts, and human review alone takes time The team wants to quickly separate real attacks from false positives It is necessary not only to output classification results, but also to judge which decisions can be processed automatically Accuracy may drop over time or due to environmental differences, even if accuracy is high during training 4. Proposed Methods and Architecture The research combined an AI that classifies alerts with another AI that checks the certainty of the ans
ShieldBreak (CVE-2026-69414): Unpatched Local Vulnerability for Privilege Escalation from Defender to SYSTEM 1. Basic Information Article Title : Microsoft working on Defender patch for ShieldBreak zero-day Publisher : BleepingComputer Publication Date : 2026-08-17 Severity : Critical Original Source : BleepingComputer Related Sources : Microsoft MSRC – CVE-2026-69414 , Public PoC Repository (403 at time of check) Related Malware & Threat Groups : None / Unidentified CVE & Products : CVE-2026-69414, Microsoft Malware Protection Engine, Microsoft Defender, Windows 10, Windows 11, Windows Server 2. Executive Summary This is an unpatched vulnerability. An attacker who already runs low-privilege code on a device can abuse a Defender flaw to escalate privileges to SYSTEM. A public PoC exists, but active exploitation has not been confirmed. 3. Attack Flow Chain Confirmed in Public Materials An attacker gains low-privilege local execution on a Windows device using another method. The attacker runs the public PoC or equivalent code on the device. The code abuses an incomplete fix for CVE-2026-50656 (RoguePlanet) in the Defender Malware Protection Engine. The context shifts from low privilege to SYSTEM. Inference: The attacker uses the gained SYSTEM privileges for post-exploitation tasks such as stealing credentials, disabling security features, and establishing persistence. Exact exploit primitives, target objects, and internal processing steps up to SYSTEM execution are not confirmed in available public materials. 4. Attacker Position and Execution Location The attacker must already be in a position to run low-privilege code on the target device. The exploit runs locally on the target Windows device. Vulnerable processing happens within the Microsoft Malware Protection Engine / Defender. There is no information that CVE-2026-69414 alone allows remote initial access. 5. Visibility for Victims and Administrators Victims and Users Privileges may change without extra user acti