/ Prompt Injection: AI’s Biggest Security Risk

Prompt Injection: AI’s Biggest Security Risk

When researchers found that Microsoft 365 Copilot could be tricked into leaking corporate data from a single email, the flaw got a clean public identifier: CVE-2025-32711, severity 9.3. When a bug hunter coaxed ChatGPT into producing valid Windows product keys by framing the request as a guessing game, it got nothing. 

Both were prompt injections. Only one is trackable. That Vulnerability Tracking Gap in AI Security, and what it costs defenders, is the subject of this article.

What Is a CVE and Why Does It Matter for Software Security?

A CVE (Common Vulnerabilities and Exposures) is a unique public identifier for a specific software flaw. It gives the whole industry one name for one bug, so a researcher in Berlin and an analyst in Bahrain know they mean the same thing.

The Role of MITRE’s CVE Program in Traditional Vulnerability Management

The CVE program is run by the MITRE Corporation, a US nonprofit. Since 1999 it has assigned hundreds of thousands of IDs, each tied to a discrete, reproducible defect in a defined product and version.

A CVE is the connective tissue of coordinated disclosure: a researcher reports the flaw, the vendor patches it, the ID is published, and defenders map it to their own assets. Without that shared label, the same bug ends up with three names and no clear owner.

The National Vulnerability Database (NVD) and CVSS Scoring

The National Vulnerability Database, maintained by NIST, enriches each CVE with a CVSS (Common Vulnerability Scoring System) score from 0 to 10. That lets teams triage: a 9.3 jumps the queue, a 4.0 waits.

Why Prompt Injection Breaks the Traditional CVE Model

The CVE model assumes a bug lives in code, sits in a version, and can be fixed. Prompt injection violates all three.

Prompt Injection as a Class of Attack, Not a Discrete Bug

Prompt injection smuggles instructions into the data an LLM reads, so the model follows the attacker rather than the user. OWASP ranks it as LLM01, the top entry in its 2025 Top 10 for LLM Applications. It is a property of how language models work, not one line of faulty code, so you cannot file a CVE against it.

A SQL injection either works or it does not. A prompt injection might succeed nine times in ten, fail on the eleventh, then stop working after a silent model update, which makes the “reproducible” part of reporting genuinely hard.

Model Versioning vs. Software Versioning

Software has clean version numbers. A weight update to a hosted model can ship silently, with no version a researcher can cite. Two calls to “gpt-4o” a week apart may not behave the same way, and there is no changelog to point at.

Why “Patching” an LLM Differs From Patching Code

Patching code closes a specific hole. A developer rewrites the faulty line, ships the diff, and the exploit path is gone for good. That clean, binary, auditable loop is the entire premise on which the CVE system rests. “Patching” a model offers none of it. There is no single line to fix, because the behavior the attacker abused is the same behavior that makes the model useful: it reads text and follows instructions. A vendor’s only levers, retraining, hardening the system prompt, or wrapping the model in input and output guardrails, all lower the odds of a successful attack rather than removing the possibility.

The fix reduces the success rate from 80 percent to 5 percent and marks it as remediated. The hole is narrower, not closed.

The recent record shows how thin that margin is. EchoLeak got past Microsoft’s dedicated cross-prompt-injection classifier by hiding its exfiltration channel in reference-style Markdown that the filter did not recognize, and the AgentFlayer exploit slipped through OpenAI’s URL safety check by routing stolen data through trusted Azure Blob Storage links. Each guardrail worked against the obvious version of the attack and fell to a rephrasing. There is a tuning tax on top of that: crank the filters too tight and the model starts refusing legitimate work, so vendors settle for a balance point rather than elimination. 

The practical takeaway is to treat “we’ve addressed this” as risk reduction, not closure.

SOC 2, ISO 27001 and HIPAA done for you. Fixed fee, 100% audit pass rate.

Audit-ready in 6 weeks. Not 6 months.

The Current State of AI Vulnerability Tracking

Several frameworks exist. None is a true registry of individual, citable prompt injection vulnerabilities.

OWASP LLM Top 10 and the LLM01 Classification

The OWASP GenAI Security Project’s LLM01:2025 entry is the most cited reference point. It is a category, not a catalog: it does not enumerate specific incidents with IDs.

MITRE ATLAS for Adversarial AI Threats

MITRE ATLAS is an ATT&CK-style knowledge base of adversarial tactics against AI systems, documenting 16 tactics and more than 80 techniques with real-world case studies as of late 2025. It maps how attacks work, but is not a per-vulnerability ledger with scores.

AVID (AI Vulnerability Database) and Its Limitations

AVID, run by a nonprofit, is the closest thing to a dedicated AI vulnerability database, cataloging failure modes with reproducible evidence. But it leans on community submissions, skews toward bias and broader failure modes, and notes that the definition of an “AI vulnerability” is itself still a working one.

Vendor-Specific Disclosures vs. Industry-Wide Registries

Disclosure happens vendor by vendor. OpenAI patched the Windows-key jailbreak server-side; Microsoft fixed EchoLeak and issued a CVE. There is no common venue where these land side by side.

 

The Consequences of No Shared Threat Registry for Prompt Injection

Fragmented Disclosure Across AI Vendors

Each lab discloses on its own terms, on its own blog, if at all. A defender protecting a multi-model stack has to monitor a dozen channels and hope nothing slips by.

Duplicate Discovery and Wasted Research Effort

Researchers rediscover the same attack repeatedly. The guessing-game jailbreak, the “dead grandma” trick, and other framing attacks are variations on one theme nobody numbered.

No Standardized Severity Scoring for LLM Attacks

CVSS was built for deterministic flaws. There is no agreed way to score an attack that is probabilistic and context-dependent, so “how bad is this” has no common answer.

Slower Defender Response Times

Without a feed to subscribe to, teams learn about LLM attacks from news and conference talks rather than a structured alert.

Challenges for Enterprise Risk Assessment and Procurement

Buyers cannot ask a vendor, “which known prompt injection issues affect your product, and are they fixed?” the way they can with CVEs. That makes enterprise risk assessment and procurement an exercise in trust rather than evidence.

Why a CVE-Like System for Prompt Injection Is Hard to Build

Reproducibility Challenges Across Model Updates

A CVE entry promises that anyone can reproduce the flaw. That guarantee is what lets a researcher verify it, a vendor confirm it, and a defender test whether their own systems are exposed. A hosted model breaks the promise on both ends. The same prompt can fail on the eleventh attempt because of normal sampling variance, and the weights themselves can change between Tuesday and Wednesday with no version bump to point to. A proof of concept that worked at disclosure may quietly stop working a week later, not because anyone fixed it, but because the model drifted. An identifier is only as useful as the thing it points to, and here the thing keeps moving.

Closed-Weight Models and Disclosure Asymmetry

With closed-weight models from OpenAI, Anthropic, Google, and others, only the lab sees the internals. Outsiders report behavior; the provider decides what to confirm and disclose. That puts the entity with the most information in sole control of how much reaches the public, and the incentives do not favor openness. Confirming a flaw invites scrutiny, while a silent server-side fix attracts none. A neutral registry depends on independent parties being able to validate and publish, and closed weights leave them able to observe symptoms but never inspect the cause.

The Blurred Line Between Bug, Feature, and Misuse

Is a model following an instruction inside a document a bug, or is the feature working as designed? A registry needs a clear yes or no on “is this a vulnerability,” and prompt injection rarely offers one. The model is doing exactly what it was built to do: read text and act on it. Whether that counts as a defect depends entirely on context the model cannot see, namely, whose instruction it was and whether the user wanted it followed. That ambiguity also gives vendors an easy out, since “working as intended” is a defensible label for behavior nobody can cleanly call broken. A catalog cannot index something the industry will not agree to name.

Shared Responsibility Between Model Providers and Application Developers

A prompt injection usually turns dangerous only when an application wires the model to tools, data, and actions through RAG, connectors, or agents. Responsibility is split between the provider and the developer, and neither side owns the whole failure. The model provider can argue the model behaved normally, and the integration was unsafe; the developer can argue they were relying on the model to resist manipulation. Both have a point, which is precisely the problem. With no clear owner, there is no clear party to file the disclosure, assign the severity, or ship the fix, and the issue falls into the gap between them.

SOC 2, ISO 27001 and HIPAA done for you. Fixed fee, 100% audit pass rate.

Audit-ready in 6 weeks. Not 6 months.

Proposed Frameworks for an AI Threat Registry

Extending CVE to Cover Model-Level Vulnerabilities

One option is to stretch the existing CVE schema to cover model behaviors, accepting probabilistic, version-fuzzy entries. It reuses trusted infrastructure but strains reproducibility norms.

Creating a Dedicated Prompt Injection Disclosure Standard

Another is a purpose-built standard with its own identifiers, severity model, and reproducibility rules, designed for non-determinism from the start.

Lessons From CVE Numbering Authorities (CNAs) Applied to AI Labs

The CVE program already delegates ID assignment to CNAs (CVE Numbering Authorities), often the vendors themselves. AI labs could become CNAs for their own models, issuing identifiers under shared rules, as Microsoft does for Copilot.

Coordinated Vulnerability Disclosure (CVD) for LLMs

Underpinning all of it is Coordinated Vulnerability Disclosure: agreed timelines, safe harbor for researchers, and a standard report format adapted to AI’s quirks.

 

What Enterprises Can Do Until a Registry Exists

Building Internal Prompt Injection Threat Catalogs

Keep a catalog of every injection technique that affects your deployed AI, with prompts, conditions, and mitigations, so you are not rediscovering attacks each quarter.

Subscribing to AI-Specific Threat Intelligence Feeds

Follow AI security research as a dedicated intelligence stream, not incidental news. Outlets like Wired and academic preprints on arXiv tend to surface novel attacks well before any vendor advisory does.

Participating in AI Red Team Communities

Red teaming is the only reliable way to know how your specific stack fails. Testing against your own guardrails, RAG pipelines, and agents finds issues no external list would hold.

Tracking OWASP, MITRE ATLAS, and AVID Updates

Treat OWASP’s LLM Top 10, MITRE ATLAS, and AVID as your standing reference set and check them on a schedule.

Pro Tip: Map your internal catalog to ATLAS technique IDs and OWASP LLM categories as you build it. When a real standard arrives your records translate instead of needing a rebuild, and meanwhile auditors get a recognized vocabulary to assess against.

Pro Tip: Map your internal catalog

Map your internal catalog to ATLAS technique IDs and OWASP LLM categories as you build it. When a real standard arrives your records translate instead of needing a rebuild, and meanwhile auditors get a recognized vocabulary to assess against.

The Path Forward: Standardizing AI Vulnerability Disclosure

Industry Collaboration Between AI Labs, Researchers, and Regulators

No single lab can run a credible cross-vendor registry; rivals will not report into a competitor’s database. It needs a neutral steward, plausibly MITRE or a NIST-backed consortium, with labs participating as authorities.

Regulatory Pressure From the EU AI Act and NIST AI RMF

The EU AI Act imposes obligations on high-risk and general-purpose AI, including incident reporting, while the NIST AI Risk Management Framework and ISO/IEC 42001 push toward documented, auditable AI risk processes. Structured disclosure is the natural next requirement.

A Call for a Public AI Vulnerability Database

The destination is a public, neutral, AI-native vulnerability database: shared IDs, a severity model built for probabilistic attacks, and disclosure rules every major lab signs onto. We are not there yet, so everything above is a stopgap.

 

Conclusion

Prompt injection is the top-ranked risk in AI security and the least trackable. It earns a CVE only when it surfaces inside a discrete product; the model-level root cause and server-side fixes leave no public trace. Until the industry builds an AI-native registry with a severity model fit for non-deterministic attacks, defenders must stitch together OWASP categories, ATLAS techniques, AVID entries, and their own catalogs. Build that internal catalog now. It is the one piece you fully control.

Frequently Asked Questions

Is there a CVE for prompt injection?

Sometimes. When it manifests in a specific product, like EchoLeak in Microsoft 365 Copilot (CVE-2025-32711) or CurXecute in Cursor (CVE-2025-54135), it can receive a CVE. The general attack class against language models has none.

Because it is a class of behavior, not a discrete code defect. It is probabilistic, it can break across silent model updates, and it often has no single fix, all of which clash with the reproducibility a CVE assumes.

AVID is the nearest dedicated database, while OWASP’s LLM Top 10 and MITRE ATLAS are the dominant classification frameworks. None is a complete, citable registry of individual vulnerabilities.

Inconsistently. Some patch silently server-side, some publish blog write-ups, and some issue CVEs when the flaw sits in a versioned product.

No. ATLAS catalogs tactics and techniques, not individual scored vulnerabilities, so it complements a CVE-style registry rather than replacing one.

Possibly. AI labs could act as CVE Numbering Authorities, but the non-deterministic nature of prompt injection makes full coverage unlikely without a purpose-built standard.

Axipro Author

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Pedro Dias

Pedro has been writing online for over 10 years. With experience in all things programming, cyber security, and compliance, he is our editor-in-chief at Axipro.

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Enforcement of the EU AI Act’s core rules started on 2 August 2026, and ISO/IEC 42001:2023 is the standard companies reach for when they need to prove their AI governance actually holds up. It’s the first certifiable standard for an Artificial Intelligence Management System (AIMS), and consultancies package help with it in two ways. A gap analysis tells you how far you are from the standard. Full implementation support builds the management system with you until you’re ready for certification. The two engagements differ enormously in cost, duration, and how much of the work the consultant carries, so picking the wrong one is expensive in both directions. Buy implementation when you only needed a roadmap and you pay for work your team could have done themselves. Buy a gap analysis when you have nobody to close the gaps and the report sits in a drawer while your certification deadline slips past. This article covers what each service includes, what each costs, who should pick which, and how the two combine. What Is an ISO 42001 Gap Analysis? A gap analysis is a structured baseline assessment. A consultant reviews your current AI governance practices against the requirements of ISO 42001: the management system clauses (4 through 10) and the Annex A controls, of which there are 38 grouped under nine control objectives. You end up with a clear picture of what already satisfies the standard, what partially satisfies it, and what doesn’t exist at all. The purpose is diagnostic, not corrective. Nobody writes your AI policy during a gap analysis. What you get is a gap report with maturity scoring against each clause and control, a prioritized remediation roadmap, an early view of your likely AIMS scope and Statement of Applicability (SoA), and an estimate of the effort certification will take. Timeframes are short. 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A typical engagement covers the AI policy and governance framework, an AI risk assessment methodology, completed AI risk assessments and AI impact assessments for your in-scope systems, the Statement of Applicability, the applicable Annex A controls put in place (data governance, human oversight, transparency, and so on), the documentation and evidence set an auditor will ask for, staff training, an internal audit, a management review, and corrective action plans for whatever the internal audit surfaces. Most providers, Axipro included, also coordinate directly with the accredited certification body through the Stage 1 and Stage 2 audits. Most organizations need roughly three to six months. It’s shorter where an ISO 27001 ISMS already exists to integrate with, longer for complex or high-risk AI portfolios. Consultant involvement is heavy and sustained, but your team doesn’t disappear from the project. 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Cost and time to readiness A gap analysis finishes

The Average Cost of ISO 42001 Consulting

Here are real numbers to anchor on: Axipro delivers ISO 42001 readiness for $4,000 if you’re under 50 employees and $5,500 if you’re over, and the GRC platform plus accredited audit adds roughly $4,000 to $7,000 on top. A mid-sized tech firm lands at around $10,000 to $15,000 all-in for year one. A small team comes in under $10,000. If you’ve been researching this topic, those figures probably look wrong to you. Published cost guides quote $85,000 to $320,000 for mid-market ISO 42001 certification. This article explains the gap: those guides price a traditional consulting-led engagement, where consultants bill day rates to build everything by hand. Automation-supported delivery, where a GRC platform collects the evidence and a fixed-fee team does the thinking, produces a completely different number. We break down both models phase by phase so you can budget against the delivery model you actually intend to buy. 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Typical Deliverables from an ISO 42001 Consultant​ Expect a defined AIMS scope statement, an AI system inventory and risk register, AI impact assessments for in-scope systems, a policy and procedure set mapped to Annex A, a Statement of Applicability, training materials, an internal audit report, and audit-day support. If a proposal can’t name its deliverables this concretely, that tells you something about how well the consultant knows the standard. How Mid-Sized Tech Firms Differ from Startups and Enterprises Mid-sized firms sit in an awkward middle. They run more AI systems across more teams than a 15-person startup, so scoping, interviews, and evidence collection all take longer, and fixed-fee providers price them in a higher tier as a result. Unlike enterprises, though, they rarely need multi-site audit sampling or a dedicated AI governance function, so the six-figure quotes written for enterprises don’t apply to them either. Average Cost of ISO 42001 Consulting Typical Price Range for Mid-Sized Tech Firms​ Two delivery models, two price ranges. Automation-supported, fixed-fee delivery: readiness consulting at $4,000 for companies under 50 employees and $5,500 for companies over 50, covering the engagement from gap analysis through certification support. The GRC platform and accredited audit add roughly $4,000 to $5,000, so a mid-sized firm’s first-year total comes to around $10,000 to $12,000. Traditional consulting-led delivery: $25,000 to $80,000 in consulting fees alone for a mid-sized firm, built on day rates of $1,000 to $1,800 across 15 to 40 consultant days. This is the model behind the $85,000-plus totals in most published guides. It still makes sense in a few situations: on-prem infrastructure the platforms can’t see, heavy regulatory overlays, or a board that wants a named Big Four partner on the engagement. 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It’s more useful to treat it as four philosophies that answer the same question in incompatible ways.   European Union China United States Gulf (KSA, UAE) Instrument One horizontal law (EU AI Act) Sequence of targeted departmental rules State laws, voluntary frameworks, sector rules Data law plus procurement requirements Enforcer Commission, national authorities, notified bodies CAC and partner ministries States, regulators, courts, buyers SDAIA, NDMO, central banks, tender owners Core concern Fundamental rights, product safety Content security, data sovereignty Liability, consumer protection National strategy, data sovereignty, state procurement Speed Slow to write, long lead times Fast, iterative, hardening Uneven, litigation-led Fast: effective when a tender says so What travels Conformity assessment, technical files Filings and labeling rarely reusable Assurance reports, questionnaires ISO certification as procurement signal The European Union: One Law for Everything The EU chose a single horizontal statute, Regulation (EU) 2024/1689, better known as the EU AI Act. It classifies AI systems into risk tiers, bans a short list of practices outright, and attaches heavy obligations to high-risk systems: risk management, data governance, human oversight, technical documentation, and conformity assessment. It applies extraterritorially, so a Bahraini or American provider whose system reaches EU users is in scope. The model’s strength is predictability, and its weakness is pace. Prohibitions have applied since February 2025 and general-purpose AI obligations since August 2025, with Commission enforcement beginning in August 2026. The 2026 digital omnibus agreement then deferred the main high-risk deadlines to December 2027 and August 2028. The EU writes slowly, publishes a timetable, and expects the world to plan around it. China: Regulation One Risk at a Time China has no single AI statute and doesn’t appear to want one yet. Instead, the Cyberspace Administration of China and partner ministries have issued targeted rules in rapid sequence: algorithmic recommendation provisions in 2022, deep synthesis rules in 2023, interim measures for generative AI services the same year, AI content labeling requirements in September 2025, and rules for anthropomorphic AI interaction services that took effect in July 2026. Each rule attacks one risk scenario, takes effect quickly, and gets refined through practice. The direction of travel matters more than any single measure. China’s revised Cybersecurity Law, effective January 2026, wrote AI research, training data, computing infrastructure, and risk monitoring into a foundational statute for the first time. Soft guidance is hardening into binding law, and the organizing logic throughout is content security, data sovereignty, and platform accountability rather than individual rights. For foreign companies, the compliance burden is operational: filings, security assessments, and labeling obligations that arrive with short notice and almost no grace period. The United States: The Market as Regulator The US still has no federal AI statute, and the vacuum is being filled from two directions. States are legislating, with Colorado’s AI Act as the most complete example, and sector regulators are stretching existing consumer protection, employment, and financial rules to cover AI. The NIST AI Risk Management Framework sits underneath as the voluntary vocabulary everyone borrows. In practice, the binding force in America is commercial. Enterprise buyers, insurers, and litigators enforce AI governance through security questionnaires, vendor reviews, and lawsuits long before any statute does. For a company selling into the US, the real regulator is the procurement team of your largest prospect. The Gulf: The State as Customer The Gulf model is the least covered and, for anyone selling into the region, the most misunderstood. Saudi Arabia has no horizontal AI act. It regulates AI through data law and through the state’s position as the dominant buyer in the economy. The Saudi Data and Artificial Intelligence Authority (SDAIA), established in 2019 and reporting directly to the Prime Minister, runs the show: it sets national strategy, publishes the frameworks, and steers what government tenders ask for, a far more hands-on role than most regulators play. The load-bearing rules are the Personal Data Protection Law, enforced since September 2023, and its cross-border transfer regime. Around them sit SDAIA’s AI Ethics Principles, generative AI guidelines for government entities, and the AI Adoption Framework, published in November 2025 as a mandatory baseline for public sector bodies, with a four-tier risk classification and lifecycle auditing for high-impact systems. A draft Responsible AI Policy went through public consultation in May 2026, confirming that a formal, operational regime is coming. The Kingdom designated 2026 its Year of Artificial Intelligence, and the direction across the region matches: the UAE runs an AI Seal program and its central bank requires bias testing at financial institutions, Oman’s National AI Policy entered into force in April 2025, and Bahrain has a proposed AI law in progress. The defining feature is speed through procurement. A requirement in a Saudi government tender takes effect the day the tender document is published, with no transition period and no parliamentary debate. High-risk use cases increasingly require self-assessments before tenders or go-lives. Regulation by purchase order moves faster than regulation by statute, and in state-led