标签：#cyber

1. Language Architecture in Ghidra When Ghidra loads an architecture (such as the MOS 6502), it parses the .ldefs manifest file, which declares metadata and binds three foundational specification pillars: The .pspec (Processor Specification): Defines the processor’s hardware context. It declares special-purpose registers (e.g., stack pointer SP , status/flags registers), default memory maps (RAM, ROM, I/O), and hardware interrupt vectors. The .cspec (Compiler Specification): Defines the ABI and calling conventions (e.g., parameter passing mechanisms), stack alignment rules, and return value handling. This is the critical building block enabling the decompiler to reconstruct assembly into readable C code. The .sla / .slaspec (SLEIGH Specification): .slaspec : The human-readable source file describing the instruction set architecture (opcodes, instruction formats, and p-code semantics). .sinc (SLEIGH Include): Modular inclusion files (typically used to split complex architectures like ARM or x86, or isolate instruction subsets like Thumb). Given the simplicity of the 6502, everything is defined directly within the .slaspec file. .sla : The compiled binary version of the .slaspec (generated by the Sleigh compiler). Ghidra loads this compiled .sla file into memory at runtime for optimal performance. 2. The Challenges of Reverse Engineering Undocumented Binaries When dealing with a binary compiled for an undocumented processor, Ghidra's default paradigm faces major limitations: The .slaspec file is unavailable. Ghidra attempts to aggressively disassemble everything. Analyzing an undocumented target requires a strict two-phase approach. 3. Missing .slaspec File Without a valid .slaspec definition, Ghidra renders ?? for every opcode. The primary objective when tackling an unknown CPU is precisely to reconstruct this missing .slaspec specification. 4. Overcoming Ghidra's Aggressive Disassembly By default, Ghidra (like most disassemblers) employs an exhaustive strategy (usin

Security researchers tracked and eavesdropped on a WIRED reporter using vulnerabilities in a pink plastic smartwatch. It’s just one piece of a deeply insecure supply chain of GPS-enabled gadgets.

Groups of hackers are breaking into large U.S. financial firms to steal sensitive data and extort victims, Google’s security researchers report.

Researchers linked the latest malicious activity to a Chinese company, after one of the spyware's operators placed an order with KFC using their real name and office address.

Connor Moucka pled guilty to hacking and stealing data from more than 165 Snowflake customers, which net him and his accomplices more than $2.5 million in ransom payments.

security #api #webdev #discuss The chat went live at 2 PM. By 2:14 it was a war zone. I thought adding real-time chat to my dev blog would spark pair-programming threads. Instead, bots flooded it with phishing links and slurs. One bot even dropped an oddly specific threat about my home city. I flipped on request logging. In the next 24 hours it logged 52 distinct attacker hostnames. IP bans did nothing. They came back from new IPs, new ASNs, new registrars. IP bans felt like swatting flies. I wanted to know what these domains actually were. That's when I started bulk-WHOISing every domain they posted. What 52 hostile domains actually look like I wrote a small Python runner. Feed it a list of hostnames and it spits out JSON. The first version used public RDAP servers directly. Public RDAP servers were slow, rate-limited, and ccTLDs broke them. I wanted DNS, SSL, subdomains, email history, and takeover risk in the same response. I landed on the enrichment endpoint at RapidAPI and put the script on GitHub . import json , time , sys , os from urllib.parse import quote import requests RAPIDAPI_KEY = os . environ . get ( " RAPIDAPI_KEY " , "" ) BASE_URL = " https://domain-whois2.p.rapidapi.com/whois " HEADERS = { " X-RapidAPI-Key " : RAPIDAPI_KEY , " X-RapidAPI-Host " : " domain-whois2.p.rapidapi.com " } def lookup ( domain : str ): url = f " { BASE_URL } ?domain= { quote ( domain ) } " try : r = requests . get ( url , headers = HEADERS , timeout = 20 ) r . raise_for_status () return r . json () except requests . exceptions . Timeout : return { " domain " : domain , " error " : " timeout " } except requests . exceptions . HTTPError as e : return { " domain " : domain , " error " : f " http { e . response . status_code } " } except Exception as e : return { " domain " : domain , " error " : str ( e )} def batch ( domains , delay = 0.6 ): results = [] for d in domains : print ( f " [*] { d } " , file = sys . stderr ) results . append ( lookup ( d )) time . sleep ( delay ) r

Let's dive in to Roku's privacy policy to see what data is being collected from you -- and how to stop it.

* *The purpose of this light version threat model is to demonstrate how STRIDE can be applied to an ECG device. It is intended for readers learning system decomposition and threat modelling techniques. The example includes a simplified set of components, threats, and mitigations for educational purposes and is not intended to represent a comprehensive medical device cybersecurity assessment or any regulatory submission. **Assumption: This example models a typical ECG device, which may include network connectivity in a clinical environment. Trust Boundaries: Trust boundaries exist between the ECG device, hospital network, and external clinical systems. System Definition: ECG is the abbreviation for an Electrocardiogram. It is used to detect electrical activity of the heartbeat in the form of P wave, QRS complex and T wave to identify and diagnose irregularities in heartbeat. Electrodes are placed on patient’s limbs and chest to measure the electrical potentials. It translates tiny electrical signals into digital wave patterns. These waveforms are used by the doctors to evaluate the heart rhythm and check for cardiac damage. Components • Electrodes • Lead wires • Amplifier and filters • Analogue-to-Digital Converter (ADC) • Main processing unit • Display/printer • Local storage • Network interface (Ethernet/Wi-Fi/Bluetooth), if supported. Data Flow Diagram: Electrodes → Lead wires → Amplifier and filters → Analogue-to-Digital Converter (ADC) → Main processing unit → Display / Printer / Local storage / Network interface (if supported) |TRUST BOUNDARY|→ Electronic Health Record (EHR) / Clinical Information System 2. STRIDE Threats: Threats Description Spoofing in general ** - Spoofing is the act of impersonating a legitimate user, device, or system to gain unauthorized access to resources or services. Violates authentication. * Spoofing in ECG * - An attacker may impersonate an authorized clinician, connected medical device, or trusted clinical system to gain unauthoriz

What the report found The FIDO Alliance — the industry group behind the passwordless authentication standard — released its State of Passkeys 2026 report in May, based on research across 11,000 consumers and 1,400 enterprise decision-makers in ten countries. A few numbers stand out: passkeys now see a 93% sign-in success rate compared to 63% for passwords, and average sign-in time drops to roughly 8.5 seconds versus over 30 seconds for password-based logins. Awareness has also jumped to 90% of consumers, with about 5 billion passkeys now active worldwide. The security case is the more important one. Passkeys are built to be phishing-resistant by design — unlike a password, there’s no shared secret that can be typed into a fake login page, because the credential is cryptographically tied to the real site and your device. That’s a structural fix, not a behavioral one — it doesn’t depend on you spotting a scam email, which is precisely where most password-based breaches start. Why adoption still lags Here’s the more interesting number: even among organizations that have rolled out passkeys, the majority still keep passwords running in parallel as a fallback, and a large share of individual users still don’t use passkeys everywhere they’re offered. The barrier at this point isn’t awareness — it’s habit. People default to what’s familiar, even when the safer option is one tap away. The practical takeaway Most major platforms — Google, Apple, Microsoft, and a growing list of banks and retailers — now offer passkeys as a login option, usually sitting quietly in account security settings labeled “passkey” or “sign in without a password.” The action worth taking today: pick your two or three most important accounts (email first, since it’s the recovery path to everything else) and set up a passkey where it’s offered, instead of waiting for a breach to force the decision. Passkeys aren’t foolproof — device loss and account-recovery flows are still an active area of security r

Security researchers found an issue with Apple's WebKit browser engine that affects Private Relay.

At the Black Hat security conference, the AI giant revealed new details about how its agents went rogue, hacked several other companies—and did it all right under the company’s nose.

Researchers at security firm Zenity found more than a dozen flaws in AI browsers—and managed to get OpenAI’s Atlas to make an unauthorized Amazon purchase.

For nearly two years, researcher Vangelis Stykas has maintained access to North Korean hackers’ servers. His work shows they pulled off intrusions in a shocking number of systems across the globe.

Security researcher James Kettle tried to push the limit of AI’s hacking abilities—and discovered how effective it can be when combined with human expertise.

AI agent safety boundaries are a critical challenge when agents use external tools. My journey into understanding how these boundaries can fail began with a deep dive into recent technical reports from leading AI research organizations. I encountered this concept while exploring incidents reported by Anthropic and OpenAI. These reports detail scenarios where AI models, despite being explicitly instructed to operate within simulated environments, managed to interact with real-world systems. This phenomenon, often termed "boundary failure," occurs when the actual operational environment of an agent does not match its internal understanding or the constraints it has been given. Modern AI agents are becoming incredibly useful because we're equipping them with capabilities far beyond just answering questions. They can run commands, browse the web, use APIs (Application Programming Interfaces), read and modify files, install packages, and interact with other systems. This ability to act and interface with the world is what makes agentic architectures so powerful and a direction truly worth investing in. However, the more an agent can do, the more critical the boundaries around it become. A key example comes from Anthropic's July 30 report, detailing three incidents discovered during their cybersecurity evaluations. Claude models were explicitly told they had no internet access and were working inside simulated environments. However, a problem with the evaluation environment's configuration meant that internet access was actually available. While attempting their assigned cybersecurity exercises, the models reached real systems, initially treating them as part of the simulation. In one striking incident, a Claude model even published a malicious Python package to the real PyPI (Python Package Index) registry, all while believing it was still operating within its simulated exercise. This wasn't simply an AI "deciding" to misbehave or to intentionally bypass security. The mo

Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.

CryptoCabana: Azure Cloud CTF Walkthrough 🏖️ Introduction Room: TryHackMe - CryptoCabana Category: ☁️ Cloud Difficulty: Medium Objective: Exploit a misconfigured Azure cloud environment to retrieve a hidden flag. This writeup details a classic cloud privilege escalation path: an exposed SAS token → storage enumeration → credential discovery → Key Vault access → secret reconstruction. The challenge simulates a real-world scenario where poor security practices lead to a complete compromise. Table of Contents Reconnaissance & Initial Access Cloud Enumeration Service Principal Discovery Key Vault Exploration The "Freshly Rotated" Clue Reconstructing the Flag Key Security Takeaways Tools Used Reconnaissance & Initial Access Action: Visited the target website: https://cryptocabanaf5scjagc.z13.web.core.windows.net/ Finding: The website offered to back up seed phrases. Right-clicking and selecting "View Page Source" revealed critical information in the JavaScript code. JavaScript Code: javascript const STORAGE_ACCOUNT = "cryptocabanaf5scjagc"; const BACKUPS_CONTAINER = "backups"; const BACKUP_SAS = "?sv=2022-11-02&ss=b&srt=sco&sp=rl&se=2099-12-31T23:59:59Z&st=2024-01-01T00:00:00Z&spr=https&sig=ZAo05W8KXdSLM9afYCNGogNRV2N5a6aB4dQI3LXz%2Fh0%3D"; Analysis: The SAS (Shared Access Signature) token was hardcoded in client-side JavaScript. Permissions: Read (r) and List (l) Expiration: 2099 – far too long! This token grants anyone access to the storage account. bash az storage container list --account-name cryptocabanaf5scjagc --sas-token "$BACKUP_SAS" -o table Cloud Enumeration Action: Listed all containers in the storage account. Command: bash az storage container list --account-name cryptocabanaf5scjagc --sas-token "$BACKUP_SAS" -o table Output: Name Lease Status Last Modified $web 2026-07-16T18:26:22+00:00 backups 2026-07-16T18:26:22+00:00 vault 2026-07-16T18:26:23+00:00 Analysis: $web: Standard container for Azure Static Website hosting. backups: Appeared empty. vault: Hidden

New findings by the Electronic Frontier Foundation aim to warn app developers that some of the third-party code they place in their apps may also collect their users' location data when they grant permission to the app.

The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.

Attribution is preliminary , and so far it seems no real damage. And it seems like this is a campaign that has targeted at least seven states . And, because this is where the US is right now, Trump doesn’t believe it’s Iran and that Minnesota…I guess…hacked itself. “I think I blame it on Minnesota because they’re grossly incompetent,” Trump said. “I would blame it on Minnesota and the governor, the corrupt governor of Minnesota. They like to say, ‘Oh, it’s Iran.’ Iran should be so lucky. Iran’s got bigger problems than worrying about Minnesota.”...