标签：#SEC

tinyNpm is a vs code extension that helps protect you from supply chain attacks, stale packages, and bloated code! I had been using package.json version keepers for quite some time but after the big supply chain attack i thought they would be the perfect place to add in some security. The idea is just to provide the latest package number x days old. This will help prevent most of the danger in supply in chain attacks. It will also remove the ^ if you have it so you can better control what version of a package your application is using. To be more security focused it gives general hints in the hover menu to help keep an eye on the packages you have installed. These hints include warnings for staleness, high dependency count, and number of downloads. Since all of this is something you can get through the npm api, I called it tinyNpm You can download it on the marketplace

TL;DR. Bringing AI coding agents into a German operation is not only a technical decision. The moment they meet real systems and real teams, they also meet three things that international AI content almost never mentions: a data processing agreement, the works council, and the question of who owns the generated code. None of this is a footnote. It decides whether your AI project reaches production or stalls in legal review. Here is the layer most vendors forget, and the fast order to clear it. Why this layer is invisible from outside Germany Most content about AI coding agents comes from a world where a developer tries a tool and starts shipping. In a German company with a works council, GDPR, and a legal department reading along, the path to production looks different. Not harder, but with stops that nobody skips without regretting it later. This is not a brake. It is the actual difference between an impressive demo and something your company is allowed to run. And it is exactly the layer that reveals whether a vendor has ever delivered in Germany or is only demonstrating a tool. The three stops The data processing agreement: who is processing whose data? As soon as personal data is processed on your behalf, Article 28 GDPR requires a data processing agreement with the processor (in German, an Auftragsverarbeitungsvertrag, or AVV). With AI coding agents the decisive question is not "are we using AI" but what does the agent actually touch. Does it run over a codebase that contains customer data? Does it send fragments to a third-party model provider? Who in that chain is the controller, and who is the processor? This is answerable, and it belongs settled before the first access, not after. A clean setup often limits, at the technical level, what data an agent can even see, which makes the data-protection question smaller and the answer simpler. The works council: codetermination over systems that can monitor This is the stop most often missed from outside Germany, b

A memo obtained by WIRED, issued by the water utilities information sharing group WaterISAC, links dozens of cyberattacks against Minnesota water utilities to Tehran.

Exploits can give persistent server access that survives credential rotation and disk re-imaging.

The health tech data giant, which handles vast amounts of patients' medical data, said hackers struck one of its protected health data stores.

As experts have warned for the last two years, some companies — like Microsoft and now Google — are finding and patching an exponential number of bugs in their products, thanks to the use of LLMs and AI tools.

Agent security today is inherited from the connection. N-AALP makes the message itself carry identity, authorization, approval and audit, verifiable offline, on any transport. Your agent just deleted a production table. The audit log says the request was approved. Now prove it. Not "show me the log line" - prove it, to someone who does not trust your log, your gateway, or your database. Which key approved it? Were those the exact arguments that were approved, or did something rewrite them after approval? Was that approval already used once? If your answer to any of those starts with "well, our gateway checks that," then the proof lives in your infrastructure, not in the message. Replay the message somewhere else and the proof is gone. This is the gap I have been working on. It has a name worth stating plainly: agent security today is inherited from the connection. TLS tells you the tunnel was private. mTLS tells you which service opened it. A bearer token tells you someone had a credential. None of that survives the message being written to a queue, forwarded by a relay, logged, replayed, or handed to a second agent. The moment a message leaves the connection it arrived on, it is just bytes with no provable origin. N-AALP is my attempt to close that. It is an application-layer object protocol where the message, not the connection, is the unit of security and governance. Full disclosure before you read further: I wrote it. I am the sole editor and maintainer, it is draft-bubblefish-naalp-00, an Independent Submission, and it claims no IETF working-group consensus. I would rather you read the spec and tell me where I am wrong than take my word for anything below. There is a section at the end listing what it does not do. The one-object idea Every N-AALP message is one signed object. Not a request type, not an envelope-plus-payload, not a header format with a body convention. One structure, one signature, one identity model, one authorization model, one audit model, an

When it's less about the product and more about the FCC.

The two Chrome updates in June patched more bugs than the 23 updates before them. Now, Google is ramping up its patching schedule thanks to AI-assisted vulnerability discovery.

Google is working on a way to apply Chrome updates without requiring you to restart your browser. In a post on Thursday, Google outlines its approach to bug fixes in the age of AI, while noting that it's trying to make updates less disruptive by "investing in 'dynamic patching' that will eliminate the need for […]

He’s being prosecuted for giving border officials a code that wiped his phone : The case centers on a feature included in GrapheneOS, a custom Android operating system that runs in place of the software on most modern Google Pixel devices. Tunick’s attorneys confirmed GrapheneOS was running on his phone. The software feature allows the device owner to set a passcode that deliberately wipes the contents of that device if entered instead of the user’s unlock passcode. Tunick’s case also raises ongoing questions about what constitutional rights can be invoked at the border, which the U.S. government has long asserted is not U.S. soil until a person is authorized to enter...

The deal gives Okta identity threat detection capabilities as enterprises seek to secure AI agents and other non-human identities across cloud environments.

A common MCP setup carries auth the same way: create an API key, paste it into mcp.json or an .env file, restart the client. It works. Now the key sits in plaintext on every machine that runs the agent. It often carries one broad, fixed permission set. Every agent that reads the file gets the same set. And when one agent misbehaves, the fix is rotate the shared key everywhere. There's a second failure that arrives after you add real auth: the agent calls a tool and gets a bare 403. The user doesn't know what to approve. The agent doesn't know what to ask for. Somebody ends up reading server logs. I build multiuser AI systems for production. My agents act on users' Gmail and Slack accounts every day - external agents like Claude Code included. None of those agents receives a provider token. This is the auth chain that makes that work, including the part that took the most design: what happens when consent is missing at call time. A URL instead of a key An external agent doesn't get the Gmail or Slack credential. It gets a URL - a managed MCP endpoint my platform exposes. I call that endpoint the door, and so does the interface further down. Claude Code connects to the door as an OAuth client. Dynamic client registration (DCR) registers its client identity against a configured redirect allowlist. The user signs in and approves the maximum this connection may be granted. The OAuth exchange returns a scoped KDCube bearer tied to that client and grant, not a provider token. The approval screen also resolves those requested capabilities to the accounts behind them. If a required provider is not connected, it is named there with a connect link. The connect step already says what to add - the same shape as the call-time denial later in this post, moved to the front. Approve it, and the connection becomes a card. That card is the whole governance relationship. Nobody registered Claude by hand, and nobody pasted a provider token. The checklist is a ceiling: the most this app

Cybersecurity experts told TechCrunch that one of the biggest lessons to be taken from the OpenAI hack against Hugging Face has nothing to do with AI, but traditional cybersecurity defense.

The U.S. federal consumer watchdog said Hims & Hers, which prescribes for sexual wellness and mental health conditions, used website trackers to share customers' information with advertisers.

How to Earn $10k+/Year from Bug Bounties tags: security, bugbounty, money, hacking How to Earn $10k+/Year from Bug Bounties: A Practical Roadmap You’ve seen the headlines: hackers finding critical flaws in billion-dollar companies and getting paid $50,000 for a single report. It looks like a magic trick, but it’s actually a skill you can build. The truth is, earning $10,000+ per year from bug bounties isn’t about being a genius coder; it’s about being consistent , strategic , and actionable . If you’re willing to treat this like a part-time job rather than a lucky gamble, hitting that $10k mark is a realistic goal within 12–18 months. Let’s cut through the noise and build a roadmap that works in 2026. The Math Behind $10k/Year Before you hunt, understand the numbers. Most beginners expect to find a critical bug worth $10,000 in their first month. That rarely happens. Instead, focus on the volume of valid findings . Low severity bugs : $100–$500 each [5] Medium severity bugs : $500–$2,000 each [5][7] High/Critical bugs : $5,000–$50,000+ [5] To hit $10,000/year , you don’t need a single critical find. You could: Find 20 medium bugs at $500 each Find 10 medium bugs ($500) + 2 critical bugs ($2,500 each) Find 40 low bugs at $250 each The key is consistency . A researcher with one year of focused hunting can realistically earn several thousand dollars annually, potentially matching a part-time income [5]. The ceiling rises steeply as you gain access to private programs , which offer higher payouts and less competition [5]. Build Your Foundation (Weeks 1–4) Don’t jump into hunting yet. You need to understand how the web actually works. Master Web Fundamentals Learn HTTP/HTTPS protocols : request/response structure, headers, cookies, session management [1] Understand client-side tech : HTML, CSS, JavaScript basics [1] Study common vulnerabilities : SQL injection, XSS, IDOR, CSRF, SSRF [1][6] Start with Free Learning Resources HackTheBox Academy (free modules) [1] TryHackMe

Build a Dependency Vulnerability Scanner with Python tags: python, security, devops, tools tags: python, security, devops, tools Build a Dependency Vulnerability Scanner with Python Your requirements.txt looks clean, but one of those dependencies might be a ticking time bomb waiting to expose your users to a data breach. You don’t need to wait for a security audit to find out—you can build your own lightweight vulnerability scanner in Python today and integrate it directly into your workflow. Security isn’t just about writing secure code; it’s about knowing what’s running in your environment. With thousands of Python packages available, the odds that you’re using a library with a known CVE (Common Vulnerabilities and Exposures) are high. Instead of relying solely on third-party tools like pip-audit or safety (which are excellent, but sometimes opaque), building your own scanner gives you full control over how vulnerabilities are detected, reported, and acted upon. Let’s build a practical, working dependency vulnerability scanner from scratch. Why Build Your Own Scanner? Existing tools like pip-audit [13], safety [10], and PySentry [4] are powerful, but they come with limitations: They may not support your specific output format (e.g., custom JSON for CI). They might not integrate cleanly with your private PyPI registry. You can’t easily tweak the logic to match your team’s risk tolerance. Building your own scanner lets you: Query the NVD (National Vulnerability Database) API directly. Parse requirements.txt , pyproject.toml , or poetry.lock files flexibly. Generate reports in any format you need (Markdown, JSON, SARIF). Fail your CI pipeline automatically when critical CVEs are found. Plus, it’s a great learning exercise in cybersecurity, API integration, and Python parsing. Step 1: Set Up Your Environment Before writing code, prepare a clean virtual environment to avoid false positives from global packages: python3 -m venv scanner-env source scanner-env/bin/activat

If the generative AI giant had followed well-known security best practices, it’s likely that its AI agent would never have escaped to the open internet and hacked multiple companies.

Drone warfare is making the skies more dangerous, even for airplanes far from the battlefield.

Researchers pitted a person against a Claude agent and found that, after a week of texting, the AI chatbot was more effective at creating “exploitable trust” with others.