Password manager Dashlane has disclosed that “fewer than” 20 users on the personal subscription plan had their encrypted vaults downloaded following a brute-force attack launched by an unknown party.
On May 31, 2026, the company said an “external” threat actor launched a brute-force attack against certain Dashlane user accounts with the aim of breaking two-factor authentication (2FA)
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Consider the history of any recent corporate scandal, and it is quite possible to guess what the story…
Hackread – Cybersecurity News, Data Breaches, AI and More – Read More
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NIST’s National Vulnerability Database (NVD) backlog mushroomed from 13,000 unprocessed security vulnerabilities in February 2024 to more than 27,000 by the end of 2025, “undermining the NVD’s utility and public trust,” according to an inspector general report.
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The European security agency’s entry to Project Glasswing is the result of “strong bilateral cooperation” between the European Commission and Anthropic.
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Part 4 of a series on creating information security policies.
Visibility before Protection
Organizations often invest heavily in cybersecurity tools: endpoint protection, firewalls, SIEM platforms, MFA, cloud security solutions, and threat detection services. Unfortunately, many security incidents still come down to a surprisingly simple problem: organizations do not fully understand what they own or where their sensitive data resides.
Before an organization can protect its environment, it first needs visibility.
(Don’t miss the Template at the end)
This is why asset management and data classification are foundational components of modern information security programs. They are not simply administrative exercises or compliance checkboxes. They are core security capabilities that directly influence risk reduction, incident response, governance, and regulatory compliance.
An aside: A good description of a Critical resource is something that is a) public-facing and b) contains important/sensitive/etc. data. On a quick search I can’t find the source for this description, but it’s something that Eric Cole said. And he’s also described it as “any asset, data, or system that is essential to the survival and primary mission of an organization or individual.” (update: I just read today, right before publishing this article, the announcement that Eric Cole passed away recently).
Many major frameworks and standards place significant emphasis on these areas. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) highlights asset management as part of the Identify function. International Organization for Standardization 27001 requires organizations to inventory information assets and establish classification procedures. American Institute of Certified Public Accountants SOC 2 evaluations frequently assess inventory management, logical access, and data handling practices. Regulations such as European Union GDPR also depend heavily on organizations understanding what personal data they possess and how it is protected.
At a practical level, the principle is simple: you cannot secure assets or information you do not know exist. Keeping track of assets became incredibly difficult and expensive when APIs came on the scene many years ago. Now, with AI agents, it’s become even more difficult and expensive!
Visibility Before Protection
A common challenge within organizations is incomplete visibility into the environment. Security teams are often responsible for protecting hundreds or thousands of systems, applications, devices, cloud services, and data repositories spread across departments and business units.
In time, environments become messy.
A cloud storage bucket created for a temporary project remains active years later. A former employee’s account is never fully disabled. An old server continues operating in a forgotten network segment. Sensitive spreadsheets are downloaded locally and shared outside approved collaboration platforms. Shadow IT solutions appear without security review.
These overlooked assets become attractive targets for attackers precisely because they are overlooked.
Threat actors are increasingly skilled at identifying unmanaged or weakly monitored systems. In many breaches, attackers do not break through the organization’s strongest defenses; they exploit forgotten assets, stale accounts, unpatched systems, or poorly governed data repositories (aka, shadow and zombie resources).
This is why asset management is FAR MORE than an IT inventory exercise. It’s a foundational security control.
The NIST CSF emphasizes this concept directly. Within the Identify function, organizations are encouraged to understand the assets, systems, data, and capabilities that support business operations. Without that visibility, risk assessments become incomplete and security priorities become reactive rather than strategic.
Similarly, ISO 27001 Annex A includes controls related to asset inventories, ownership responsibilities, acceptable use, and information classification. The message across these frameworks is consistent: visibility enables security.
Effective asset management programs typically include several core elements:
Asset inventories for hardware, software, cloud services, and data repositories
Defined asset ownership
Lifecycle tracking
Regular inventory reviews
Configuration management
Monitoring for unauthorized or unmanaged assets
Ownership matters just as much as visibility. Every asset needs an accountable owner responsible for its maintenance, access approvals, and security requirements. Assets without ownershipbecome assets without oversight.
Why Data Classification Simplifies Security
Once organizations understand what assets they possess, the next challenge is understanding the sensitivity of the information stored within them.
Not all data carries the same level of risk.
A public marketing brochure does not require the same protections as employee records, customer financial data, security architecture diagrams, or intellectual property. Without classification, organizations struggle to apply security controls consistently.
This creates two common problems.
1) Orgs may overprotect low-risk information, creating unnecessary friction and operational complexity (not to mention extra cost!).
2) They may underprotect highly sensitive information because they fail to recognize its importance. (the extra cost in #1 may lead to underfunding in #2)
Data classification solves this by creating context.
A well-designed classification program helps employees and security teams quickly understand how information should be handled, stored, transmitted, and protected. It also improves consistency across departments and technologies.
One of the easiest classification structures is something like this:
Public
Information approved for public release.
Examples include:
Website content
Press releases
Marketing materials
While public data may not require strict confidentiality protections, organizations still need to preserve integrity and accuracy.
Internal
Information intended for internal organizational use.
Examples include:
Internal procedures
Organizational charts
Operational documentation
Internal communications
This information should generally remain accessible only to authorized employees and contractors.
Confidential
Sensitive information is that info almost certain to harm the organization, employees, customers, or partners if disclosed improperly.
Examples include:
Customer records
Financial information
Employee data
Authentication credentials
Security configurations
Intellectual property
Confidential information typically requires stronger access controls, encryption, monitoring, and restricted sharing practices.
Data classification also directly supports regulatory compliance efforts.
Under GDPR, organizations are expected to understand what personal data they process and implement safeguards appropriate to the risk. Similarly, SOC 2 reports examine how organizations identify and protect sensitive information within their environments. This report contains deeply technical information and internally revealing information; it’s best to keep it closely guarded.
Classification also becomes incredibly valuable during incident response.
When a security event occurs, one of the first questions leadership asks is: “What data was affected?”
Organizations with mature classification programs can answer this much faster. They can determine whether exposed information was public, internal, or confidential, which directly influences response actions, legal obligations, customer notifications, and business impact assessments.
Handling Requirements Matter
Classification labels alone are not enough.
The real value comes from defining handling requirements that guide employee behavior and technical controls throughout the information lifecycle.
An effective Asset & Data Classification Policy needs to establish clear expectations for:
Storage
Transmission
Retention
Disposal
Access control
Encryption
Monitoring
For example, confidential data may require:
Encryption at rest and in transit
MFA-protected access
Restricted sharing permissions
Approved storage platforms
Logging and monitoring
Secure disposal procedures
Internal information may require simpler protections such as authenticated access and approved collaboration platforms.
The objective? consistency.
Don’t make employees guess how sensitive information should be handled. Policies and classifications should make expectations clear and actionable. Make sure the policies are a) centrally located and b) easily accessible.
This is more important in hybrid work environments where employees routinely access data from cloud platforms, remote locations, mobile devices, and third-party applications.
Building Security from the Ground Up
One of the most important lessons in governance, risk, and compliance is that mature security programs are built on strong fundamentals.
Advanced detection tools and sophisticated security technologies are valuable, but they cannot compensate for poor visibility and unmanaged data risks.
Organizations that struggle with asset management and data classification often experience:
Incomplete risk assessments
Weak access governance
Regulatory compliance gaps
Inefficient incident response
Increased likelihood of data exposure
Conversely, organizations with strong visibility and classification practices are better positioned to prioritize security investments, enforce consistent controls, and respond effectively when incidents occur.
The Closing
The reality: many security failures aren’t caused solely by sophisticated attacks. Many occur because organizations lacked awareness of what they owned, where critical information resided, and/or how sensitive data should have been protected.
Before organizations can strengthen defenses, deploy advanced tools, or improve detection capabilities, they must first answer two foundational questions:
What do we have?
And
How important is it?
TEMPLATE (make it your own – adapt as needed!)
(NOTE: As you see below, the policy is not very detailed. Policies are meant to be overarching and not readily changed, though they need to be reviewed regularly. People often conflate Procedures with Policies. In common parlance, it’s fine to talk of changing policies and procedures, and often the two are combined – which is just fine. But in actual terminology, the two are separate. For SMBs, life is probably easier putting the Policies and Procedures together to review regularly (typically minimum of once annually for ISO 27001). But for large orgs, enterprises, and educational institutions, it ‘s often best to separate the two. Because a Policy is a guiding document, you don’t want to have to change the principles and guidance and primary directives often at all.)
Asset & Data Classification Policy
Policy Overview
Policy Name
Asset & Data Classification Policy
Policy Owner
[Department / Security Team / Governance Team]
Policy Approver
[Executive Leadership / CISO / CIO]
Effective Date
[Insert Date]
Review Cycle
This policy shall be reviewed annually or upon significant organizational, regulatory, or technological changes.
Related Standards & Frameworks
ISO/IEC 27001
NIST Cybersecurity Framework (CSF)
SOC 2 Trust Services Criteria
GDPR
Applicable legal, contractual, and regulatory obligations
1. Purpose
The purpose of this policy is to establish requirements for identifying, classifying, handling, and protecting organizational information assets.
This policy is intended to:
Improve visibility into organizational assets and data
Support risk management and cybersecurity efforts
Protect sensitive information from unauthorized access, disclosure, alteration, or destruction
Support compliance with legal, regulatory, and contractual obligations
Define consistent security expectations for information handling
2. Scope
This policy applies to:
All employees
Contractors
Third parties with access to organizational assets or information
All organizational hardware, software, cloud services, applications, and data repositories
This policy applies to information assets regardless of:
Format
Location
Transmission method
Storage platform
3. Definitions
Asset
Any information, system, device, application, service, or resource that supports business operations.
Examples include:
Laptops
Servers
Cloud platforms
SaaS applications
Databases
User accounts
Mobile devices
Documentation
Data Classification
The process of categorizing information based on sensitivity, confidentiality, and business impact.
Asset Owner
An individual or department responsible for the management, security, maintenance, and lifecycle oversight of an asset.
4. Asset Management Requirements
4.1 Asset Ownership
All organizational assets must have an assigned owner.
Asset owners are responsible for:
Maintaining asset accuracy
Reviewing access permissions
Ensuring appropriate security controls
Supporting lifecycle management
Reporting unauthorized or unmanaged assets
4.2 Asset Inventory
The organization shall maintain an inventory of information assets, including:
Hardware
Software
Cloud services
Applications
Data repositories
Administrative accounts
Network-connected devices
Asset inventories shall:
Be reviewed regularly
Be updated when assets are added, modified, or removed
Include ownership information
Include classification where applicable
5. Data Classification Requirements
Information assets shall be classified according to the following categories:
5.1 Public
Definition
Information approved for public disclosure.
Examples
Public website content
Marketing materials
Published reports
Press releases
Security Requirements
Integrity protections should be maintained
Public disclosure must be authorized
5.2 Internal
Definition
Information intended for internal organizational use only.
Examples
Internal procedures
Team communications
Operational documentation
Internal business plans
Security Requirements
Access limited to authorized personnel
Protected from unauthorized disclosure
Shared only through approved communication methods
5.3 Confidential
Definition
Sensitive information that could negatively impact the organization, customers, employees, or partners if disclosed improperly.
Examples
Customer information
Employee records
Financial data
Authentication credentials
Security configurations
Intellectual property
Regulated data
Security Requirements
Access restricted by business need
Encryption required during storage and transmission
Monitoring and logging where applicable
Sharing limited to authorized individuals and approved platforms
6. Handling Requirements
All classified information must be handled according to organizational security requirements.
6.1 Storage Requirements
Public
May be stored on approved public-facing systems.
Internal
Must be stored on approved organizational platforms with appropriate access controls.
Confidential
Must be stored:
Using encryption where appropriate
On approved secure systems
With restricted access permissions
In accordance with retention requirements
6.2 Transmission Requirements
Public
May be transmitted through standard approved communication methods.
Internal
Should only be shared through approved organizational communication platforms.
Confidential
Must be transmitted using:
Encrypted communication methods
Approved secure file-sharing systems
Authorized recipients only
6.3 Disposal Requirements
Information assets shall be securely disposed of when no longer required.
Approved disposal methods may include:
Secure deletion
Media destruction
Data wiping
Physical shredding
Confidential information must be disposed of using secure destruction procedures approved by the organization.
7. Roles & Responsibilities
Security Team
Responsible for:
Maintaining classification standards
Supporting policy enforcement
Monitoring compliance
Providing guidance
Managers
Responsible for:
Ensuring employee awareness
Supporting classification compliance
Identifying departmental assets
Employees & Users
Responsible for:
Properly handling information assets
Following classification requirements
Reporting security concerns or unauthorized activity
8. Policy Compliance
Violations of this policy may result in:
Removal of access privileges
Disciplinary action
Contractual consequences
Legal or regulatory penalties where applicable
9. Exceptions
Exceptions to this policy must:
Be documented
Include risk justification
Receive formal approval from authorized leadership or security personnel
10. Policy Review & Maintenance
This policy shall be reviewed:
Annually
Following significant security incidents
Following major infrastructure or regulatory changes
Updates shall be approved through the organization’s governance process.
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Dutch authorities seized command-and-control servers tied to a botnet of infected computers, smartphones, and tablets that was allegedly used to power a residential proxy network and facilitate cybercrime.
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Across the frontier labs, the highest prompt injection figures published this spring are Anthropic’s. Point a red-teamer at its newest model in a browser, and the attacker hijacked it 31.5% of the time before safeguards engaged. OpenAI, Google, and Meta never gave security leaders a comparable number to set beside it. That figure looks like a liability. In this comparison, it is the opposite. It’s the one solid piece of ground.
Four frontier labs each shipped a prompt injection disclosure, and no two match. Anthropic put 244 pages and four agentic surfaces on the table on May 28. OpenAI reported one surface, connectors. Google moved the subject out of the model card and into a separate safety framework. Meta shipped no closed-model card at all. The Cross-Vendor Prompt Injection Disclosure Grid below maps what each lab tested, what each one measured, and the four places a side-by-side comparison falls apart.
A prompt injection hides a malicious instruction in something an agent reads, a web page, a document, or a tool result. One planted line can exfiltrate records or fire off actions nobody approved, and these cards are a buyer’s only first-party evidence.
There is no industry standard for measuring any of this, and that is the root of the problem. Carter Rees, VP of AI at Reputation, told VentureBeat that prompt injection breaks the assumption that every legacy tool was built on. “A phrase as innocuous as, ‘ignore previous instructions’ can carry a payload as devastating as a buffer overflow, yet it shares no commonality with known malware signatures.” With no shared signature to scan for, each lab built its own yardstick, and the results do not line up.
Adam Meyers, Senior Vice President of Counter Adversary Operations at CrowdStrike, said that the exposure is now the buyer’s to manage. “As you implement AI, it increases your attack surface, so now you have to be able to protect those AI models against adversary misuse or data poisoning or prompt injection.” CrowdStrike’s own frontline data shows the threat side is not standing still. In its 2026 Financial Services Threat Landscape Report, released in May, the company reported adversaries using AI to compress the time from initial access to impact faster than legacy defenses can respond.
Anthropic measured four surfaces. The numbers swing by an order of magnitude depending on which one you read.
The Opus 4.8 card does what others do not: It breaks prompt injection out by surface, and the spread is the story.
Put the model in a coding environment, and an adaptive attacker from Gray Swan’s Shade tool got through on 7.03% of single attempts with thinking on. Safeguards pulled that to 2.09%.
Move the same class of attack into a browser, the surface behind Claude in Chrome and Claude Cowork, and the floor gives way. Anthropic put professional red-teamers on 129 web environments held out from training and printed every result in Table 5.2.2.4.A on page 81 of the system card. Per-attempt is the share of all injection attempts that got through across 129 environments at 10 tries each. Per-scenario is the harder cut, the share of environments where at least one try landed.
Read down the per-attempt column without safeguards, thinking on, and the raw rate drops with each generation, from Sonnet 4.6 at 50.7% to Opus 4.8 at 31.5%. The lowest in the table, 5.9%, belongs to Mythos Preview, which nobody can buy yet. Turn safeguards on, and Opus 4.8 drops to 0.5%. Turn thinking off and it drops to zero across all 129 environments.
OpenAI measured one surface, with attacks it already knew.
The GPT-5.5 card, published April 23 and updated April 24, handles prompt injection in one place, a single section on robustness to known attacks against connectors. OpenAI reports it as a robustness score where higher is better, the inverse of an attack success rate. GPT-5.5 came in at 0.963, down from 0.998 for GPT-5.4-thinking. That one figure is the whole disclosure.
Anthropic tested four surfaces against an adaptive attacker that rewrites its approach based on what the model does, then ran a one-week bug bounty where red-teamers tried to break the model live. When the coding results came back worse than Opus 4.7, the card said so.
Lay the 0.963 next to the 31.5%, and they look like they belong on a scoreboard. They do not. One is a robustness score against known attacks on one surface. The other is a per-attempt attack success rate across 129 browser environments against an attacker that adapted in real time.
Google and Meta never put the number in the card at all
Google’s Gemini 3 files prompt injection under mitigations, and the launch materials describe stronger resistance with no number attached. The Frontier Safety Framework report does run red teaming, but across its capability domains, and prompt injection is not one of them. No model card, no framework page, no per-surface number a buyer can lift into a risk review.
Meta ships open weights with no closed-model card. Prompt injection defense sits in a separate stack, Purple Llama’s LlamaFirewall. A PromptGuard 2 classifier and an AlignmentCheck auditor, run against the public AgentDojo benchmark and its 97 tasks, cut attack success from 17.6% with no defense to 1.75% combined. Real numbers. They grade the guardrails on a public benchmark, not the model on a deployment surface a security team would recognize.
The Cross-Vendor Prompt Injection Disclosure Grid
The grid below works on any frontier model security teams are weighing. Each row marks a place where the four labs are split. Each split is where a quick comparison breaks. The Anthropic figures come from the Opus 4.8 system card. Everything for the other three comes from each vendor’s published safety documentation.
Dimension
Anthropic, Opus 4.8
OpenAI, GPT-5.5
Google, Gemini 3.x
Meta, Llama stack
Safety document
System card, May 28 2026, 244 pages
System card, April 23 2026, updated April 24
Model card plus a separate Frontier Safety Framework report
No closed-model card. Open weights plus the Purple Llama stack
Injection benchmark or dataset
ART from Gray Swan and UK AISI, the Shade tool, plus an internal browser eval, 129 environments
Internal connectors evaluation, known attacks
None for injection
AgentDojo, 97 tasks
Surfaces with an injection eval
Four. Tool use, coding, computer use, browser
One. Connectors
None published for injection
One. AgentDojo agent tasks
Multi-attempt escalation shown
Yes. ART benchmark at 1, 10, 100. Coding and computer use at 1 and 200
No. A single score
No
No
Headline metric and unit
Attack-success rate. Browser, with thinking, 31.5% raw, 0.5% safeguarded
Robustness score, higher is better. 0.963, down from 0.998 for GPT-5.4-thinking
Attack-success rate on AgentDojo. 17.6% baseline to 1.75% combined
Live external bounty
Yes. One-week live injection bounty with external red-teamers
No injection bounty. Bio bounty only
None found
None found
Regression disclosed
Yes, explicit, with numbers
Number fell 0.998 to 0.963, not framed as a regression
Increased resistance claimed, no numbers
Not applicable
Five factors security teams need to consider now
Anthropic tested four surfaces and printed every number. OpenAI tested one. Google printed no per-surface rate. Meta graded its guardrails, not the model. The four disclosures do not add up to a comparison. These five steps build one.
Pull every agent you have deployed or scoped and tag each by the surface it touches, browser, code, connectors, or desktop. Anthropic’s rate for Opus 4.8 runs 2.09% on coding and 0.5% on browser. A blended number covers neither. Pull the vendor’s published rate for your specific surface. If the vendor never published one, treat it as untested.
Send the Cross-Vendor grid to every vendor under evaluation. A 0.963 connectors score and a 31.5% browser rate were never on one scale. Demand a per-surface attack success rate, raw and safeguarded, with the attacker methodology named. The blank cells are the surfaces with no first-party evidence.
Confirm in writing which number your integration gets. Anthropic’s 0.5% comes from Claude in Chrome and Cowork with the full safeguard stack. On the API, the model ships without them. Do not accept a product number for an API deployment.
Add two clauses to the RFP. The vendor tested with an adaptive attacker that rewrites payloads against the model, and someone outside the company tried to break it. Anthropic ran Gray Swan’s adaptive Shade tool and a one-week paid bounty. OpenAI tested known attacks on one surface. Adversaries do not submit known payloads.
Run your own injection test before any agent ships. Vendor numbers come from vendor environments with vendor system prompts. Your stack has its own prompts, permissions, and data access. Set a pass threshold. Anything above it does not go live.
The bottom line. No standard exists for this yet. A vendor’s number tells you what it chose to measure. Your own red team tells you what you are exposed to.
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