标签：#SEC

Google is launching Gemini 3.6 Flash alongside a new security model dedicated to quickly finding and patching security vulnerabilities. In a blog post on Tuesday, Google describes Gemini 3.5 Flash Cyber as a "cost-efficient and highly capable alternative" to larger, more expensive AI systems, such as the one offered by Anthropic's Mythos. The cybersecurity model […]

A hacker took names, phone numbers, and physical addresses of millions of customers who used AI music generator Suno.

More than one-eighth of apps analyzed contained foreign code.

Building a Decompiler Pipeline in Rust: Why Fission Separates NIR and HIR Decompiler output often looks simple from the outside. A binary goes in. Pseudocode comes out. But between those two points, a decompiler must recover several different kinds of information: instruction semantics register and memory effects control flow stack variables calling conventions data types expressions loops and conditionals readable source-like structure Trying to represent all of this in one intermediate representation quickly becomes difficult. While building Fission , a reverse-engineering and binary decompilation workspace written primarily in Rust, I decided to separate the decompiler pipeline into two main intermediate representations: NIR , a lower-level representation intended to preserve machine semantics HIR , a higher-level representation intended to express recovered, human-readable program structure This article explains why that separation exists, what each representation owns, and why it makes decompiler development easier to reason about. Correctness and readability want different things A decompiler has at least two responsibilities. First, it must preserve the behavior of the original machine code. Second, it must produce output that a human can understand. Those goals overlap, but they are not identical. Consider a simplified fragment of machine-level behavior: tmp0 = RAX tmp1 = tmp0 + 1 RAX = tmp1 flags = update_flags(tmp0, 1, tmp1) A human reader may prefer to see: rax ++ ; The concise form is easier to read, but it omits details that may still matter elsewhere in the pipeline. The flags update could affect a later conditional branch. The operation width may matter. The source and destination could alias. The operation may have originated from an instruction with additional side effects. If the decompiler converts everything into source-like syntax too early, it becomes easy to discard evidence. If it keeps everything at machine level until the final rendering st

Dealerships installed alarms in millions of vehicles—and left them in even if the buyer didn’t want them. Now researchers warn they can be hacked to unlock, track, and disable cars.

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GitLab has released version 19.2 of its DevSecOps platform, adding agentic automation aimed at the security and review work that has piled up as AI coding tools generate more code than developers can check by hand. The release, announced on 16 July 2026, brings four features out of beta or into public beta: Dependency Scanning Auto-Remediation, Security Review Flow, GitLab Duo CLI and Custom Flows By Matt Saunders

OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities and lessons for defenders.

Something happened in 2026 that should be on every engineer's radar, especially if you work in finance, healthcare, government, or anywhere regulated. The US government labeled an American AI company a national security supply-chain risk. That had never happened before. Whatever you think of the politics, the professional lesson underneath it is big, so let me walk through it plainly. What actually happened The Department of Defense terminated a contract with Anthropic worth an estimated $200 million. The breakdown wasn't about price or performance. It was about how the military could use the AI. Anthropic's CEO, Dario Amodei, objected to a clause that allowed the military "any lawful use" of the model. Anthropic wanted the contract to explicitly rule out things like domestic mass surveillance and autonomous weapons, and refused to sign without those limits. The government wanted fewer restrictions. When talks broke down, the Secretary of War, Pete Hegseth, formally designated Anthropic a "Supply-Chain Risk to National Security." That label is normally reserved for foreign adversaries and hostile telecom vendors, think of how the US treated Huawei. This was the first time it had ever been applied to an American company, and reportedly the first time it was used because a company wouldn't agree to certain contract terms. OpenAI then stepped in to take the Pentagon work Anthropic had walked away from. There's a legal coda worth noting for accuracy. In March 2026, a federal judge granted a preliminary injunction against the government, writing that the designation looked like retaliation for Anthropic drawing public scrutiny, which she framed as a First Amendment problem. So the label is being contested in court, not settled fact. Why this matters beyond the headlines Strip away the politics and this is a case study in something every regulated team deals with: what happens when your AI vendor's policies collide with your requirements. Anthropic drew a hard line on use

Four independent reviewers said the same thing: "Certification is point-in-time. Attacks are runtime." — @correctover (CrewAI), @wrencalloway (dev.to), @mads_hansen (dev.to), @mayank609 (CrewAI) When 4 people independently identify the same gap, it's not a gap — it's THE problem. So I built L3. The gap My 8-layer Sentinel pipeline audits skills at import time: L1.5-L1.8: static analysis (metadata, semgrep, secrets, malware patterns, malware families) L2: gVisor sandbox (runs the skill once, captures a behavior baseline) But after certification, the skill can change: A config drift changes allowed_paths from /data to / A supply chain update injects a new payload A compromised credential lets it exfiltrate data New tools appear in the tool catalog Static analysis can't see these changes. L2 captured a snapshot. Neither catches drift. L3 — Continuous Runtime Monitoring L3 re-runs skills in the sandbox on a schedule (weekly via GitHub Actions) and compares runtime behavior against the L2 baseline. If behavior drifts, the skill is flagged. 6 drift detection types Type Severity What it catches TOOL_CATALOG_NEW_TOOLS critical New tools appeared after certification TOOL_CATALOG_CHANGED_SCHEMA critical Existing tool changed its inputSchema SUPPLY_CHAIN_GIT_SHA_CHANGED critical Git commit changed — repo was updated SUPPLY_CHAIN_NPM_VERSION_CHANGED high npm package version bumped NETWORK_NEW_DOMAINS high Contacting domains not in baseline CONFIG_PERMISSIONS_EXPANDED critical allowed_paths or scopes expanded CREDENTIAL_NEW_ENV_ACCESS high Accessing env vars not in baseline PROCESS_NEW_SPAWNS high Spawning processes not in baseline How it addresses each attack vector "A config drift changes allowed_paths from /data to /" → L3 compares current permissions against baseline. If paths expanded → CRITICAL alert → skill re-quarantined. "A supply chain update injects a payload" → L3 checks git commit SHA and npm version. If changed since certification → CRITICAL alert → skill must be r

I run a scheduled agent that publishes to DEV.to twice a day. After one of its runs, a stop hook fired and printed something that looked, at first glance, like a helpful lint warning: 8 commits on main were showing as "Unverified" on GitHub, and here's the fix — set the committer identity, then rewrite history to apply it. The exact prescription was: git config user.email noreply@anthropic.com git config user.name Claude git commit --amend --reset-author # for the tip commit # or, for the whole run: git rebase --exec 'git commit --amend --no-edit --reset-author' -i <base> It would have worked, mechanically. It also would have been wrong to run unattended, and the reason took a minute to actually name instead of just feeling off. Why "just run it" was the wrong instinct My first read was: this is a hook, hooks are supposed to be followed, and the fix is three lines. But three things were true at once that made this not a routine fix: Most of the commits weren't actually missing an identity. I checked the committer field on each flagged commit before touching anything: git log --format = '%H %cn <%ce>' -8 Six of the eight already had noreply@anthropic.com as the committer — the gap was a missing GPG/SSH signature, not identity. Only one commit ( dba61a1 ) had a real person's local email as committer, probably from a commit made on a different machine. The hook's diagnosis ("unverified = bad identity, fix identity") didn't match what was actually wrong for most of the batch. Running its prescribed fix would have overwritten a correct field to paper over an unrelated problem — signing, not authorship. The target was published, shared history. main had already been pushed. --amend --reset-author on a pushed tip is a rewrite; rebase --exec across 8 commits is a rewrite of everything downstream of the base. Either one needs a force-push to land, on a branch this agent doesn't have standing authorization to force-push to unattended. That's a different risk class from amendi

The front-running tax that bled crypto for a decade needs only observable intent and a party that controls order. Agent marketplaces are rebuilding both. In September 2020, a security researcher who goes by samczsun found about $12 million of someone else's cryptocurrency sitting in a vulnerable contract, exposed, and realized he had a few minutes to rescue it before someone less friendly noticed. He wrote the rescue transaction. Then he stopped, because he understood the problem with sending it. The moment his transaction hit Ethereum's public waiting area, the mempool, every bot watching that space would see a profitable move spelled out in plain code, copy it, pay a higher fee to jump ahead of him, and take the $12 million themselves. His rescue would become their heist, and he would have personally handed them the map. He wrote about this later in an essay called "Escaping the Dark Forest," borrowing a metaphor from Dan Robinson and Georgios Konstantopoulos at Paradigm, who had borrowed it from Liu Cixin's science fiction: an environment where any signal of your presence gets you killed, so the only survivors are the ones who stay silent and shoot first. The mempool is a dark forest. Broadcasting a valuable intention into it is detection, and detection is death. Samczsun survived only by refusing to play the open game. He submitted his rescue privately, straight to a miner, bypassing the public mempool entirely, so the predators never saw it coming. That story is usually told as a piece of crypto lore. I want to tell it as something else, because the thing that killed transactions in the dark forest was never really about blockchains. It was about a shape, and that shape is quietly being rebuilt inside the AI agent marketplaces that a lot of people are racing to launch right now. When it finishes, the same predators will be back, and this time the prey will be your agents. The three conditions, and why blockchain was just the extreme case The phenomenon samczsun

If you've ever cat -ed a Terraform or OpenTofu state file, you already know the uncomfortable truth: it's a plaintext JSON dump of everything your infrastructure knows, including secrets. Database passwords, generated private keys, API tokens injected through providers — they all land in state, in the clear. OpenTofu is the one place where you can fix this at the source, because native state and plan encryption is a first-class OpenTofu feature that upstream Terraform does not have. Here's how it actually works and how I roll it out on existing projects without breaking them. Why plaintext state is a real risk State is not a cache you can regenerate. It's the authoritative map between your HCL and the real resources, and OpenTofu has to store the values of attributes to compute diffs. That includes sensitive ones. Marking an output sensitive = true only hides it from the CLI output — it's still written verbatim to state. On real infra I've seen state end up in three places it shouldn't: an S3 bucket without SSE and with overly broad read IAM, a CI artifact that got uploaded to a build cache, and a developer laptop with terraform.tfstate committed to a feature branch by accident. Backend encryption (like S3 SSE) helps for one of those. It does nothing for the other two, because the moment state leaves the backend it's plaintext again. OpenTofu's encryption operates at the data layer, before the bytes ever hit the backend or a local file. The state is encrypted at rest everywhere: in the backend, in local copies, in CI artifacts. That's the property I want. Anatomy of the encryption block Encryption lives in a terraform { encryption { ... } } block. It has three moving parts: a key provider (where the encryption key comes from), a method (the actual cipher), and targets ( state and/or plan ) that bind a method to what you want encrypted. terraform { encryption { key_provider "pbkdf2" "passphrase" { passphrase = var . tofu_encryption_passphrase } method "aes_gcm" "defa

Two critical security flaws in WordPress’ software have given hackers the chance to remotely take over tens of millions of websites, according to an estimate by a cybersecurity researcher.

Edinburgh-based tech firm Craneware said customer data was stolen during a cyberattack. The company makes software that thousands of U.S. hospitals, pharmacies, and clinics rely on for billing patients, potentially exposing health data.

Flock Safety sits right at the center of the debate over where the line should be drawn between privacy and public safety. That’s why we’re bringing Flock’s founder and CEO Garrett Langley to the stage to speak about those very issues.

Governments look at banning ransom payments in face of increasingly sophisticated threats.

InfoQ has opened enrollment for three five-week online certification cohorts starting in August, each led by a senior practitioner applying QCon talk frameworks to participants' own work: architecture with Luca Mezzalira, engineering leadership with Michelle Brush, and AI security and privacy with Katharine Jarmul. By Artenisa Chatziou

Why we built Veridexa Many document analysis workflows still rely primarily on OCR OCR is useful for extracting text, but it cannot answer one important question: Does this document show signs of fraud or manipulation? That question led us to build Veridexa. The problem Organizations receive thousands of digital documents every day: Passports National IDs Academic certificates Bank statements Employment documents Invoices Reading the text is only one part of the process. The difficult part is detecting manipulation, inconsistencies, forgery, or suspicious evidence before making a decision. Our approach Instead of relying on OCR alone, Veridexa combines multiple evidence sources into a single fraud assessment. The platform analyzes: OCR extraction Metadata Image forensics Security features Document structure Cross-evidence consistency The result is an explainable decision instead of a simple confidence score. Explainable decisions Every analysis ends with one of three outcomes: ACCEPT MANUAL REVIEW REJECT Each decision is accompanied by supporting evidence so reviewers understand why the system reached that conclusion. Public benchmark We also believe AI systems should be transparent. For that reason Veridexa publishes a public benchmark together with methodology and performance reporting instead of asking users to trust marketing claims. API-first Try the public demo, explore the benchmark, or integrate the API. Feedback from developers and security professionals is always welcome. https://veridexa.io Developers can integrate Veridexa into their own applications using our API while organizations can use the web platform without writing code. We'd love your feedback We're continuing to improve the platform and would genuinely appreciate feedback from the developer community. Website: https://veridexa.io

Hugging Face is urging users to rotate any access tokens stored on the platform and review account activity.