VulnerabilityPublished 27 Mar 2026

CVE-2026-30304: Improper Input Validation

Source CVE Database V5

Not known to be exploited · CVSS 9.6 Critical.

Data as of 26 Oct 2026, 12:55 UTC

CVSS 3.1 base score

9.6Critical

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.4% chance of exploitation in 30 days – higher than 39% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: Required
Scope: Changed
Confidentiality: High
Integrity: High
Availability: High

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: Required: Successful exploitation of this vulnerability requires a user to take some action before the vulnerability can be exploited.
Scope: Changed: An exploited vulnerability can affect resources beyond the security scope managed by the security authority of the vulnerable component. In this case, the vulnerable component and the impacted component are different and managed by different security authorities.
Confidentiality: High: There is total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact.
Integrity: High: There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component.
Availability: High: There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 27 Mar 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

AI-suggested

To mitigate CVE-2026-30304, organizations should implement multiple layers of defense beyond relying solely on AI model classification. First, enhance the AI model's training and validation to better detect prompt injection patterns and malicious command structures. Incorporate heuristic and signature-based detection mechanisms to complement AI judgments. Second, enforce strict input validation and sanitization on all commands before execution, rejecting or flagging suspicious inputs regardless of AI classification. Third, restrict the automatic execution feature to minimal privilege environments and avoid running commands with elevated permissions. Fourth, require explicit multi-factor user approval for any command execution that could impact system integrity, even if classified as safe by the AI. Fifth, implement robust logging and monitoring to detect anomalous command execution patterns and enable rapid incident response. Finally, maintain up-to-date software versions and apply patches promptly once available. Organizations should also conduct security reviews of AI-assisted automation workflows to identify and remediate similar risks proactively.

Description

In its design for automatic terminal command execution, AI Code offers two options: Execute safe commands and execute all commands. The description for the former states that commands determined by the model to be safe will be automatically executed, whereas if the model judges a command to be potentially destructive, it still requires user approval. However, this design is highly susceptible to prompt injection attacks. An attacker can employ a generic template to wrap any malicious command and mislead the model into misclassifying it as a 'safe' command, thereby bypassing the user approval requirement and resulting in arbitrary command execution.

Impact

The primary impact of CVE-2026-30304 is the potential for unauthorized arbitrary command execution on systems using AI Code's automatic terminal command execution feature. This can lead to a wide range of consequences including system compromise, data theft, data corruption, and disruption of services. Since the attacker can bypass user approval, the risk of executing destructive commands increases significantly, potentially allowing privilege escalation or lateral movement within networks. Organizations that integrate AI Code into their development pipelines, automation scripts, or operational workflows may face increased exposure to insider threats or external attackers exploiting this vulnerability remotely or locally. The integrity and confidentiality of affected systems are at high risk, and availability could also be impacted if destructive commands are executed. The lack of authentication or user interaction barriers in the 'execute safe commands' mode exacerbates the threat. Given the growing adoption of AI-assisted coding and automation tools, the scope of affected systems could be broad, impacting enterprises, cloud providers, and software development environments worldwide.

Summary

CVE-2026-30304 identifies a critical vulnerability in AI Code's design for automatic terminal command execution. The system provides two execution modes: one that automatically runs commands deemed 'safe' by the AI model, and another that requires user approval for commands considered potentially destructive. The vulnerability stems from the AI model's susceptibility to prompt injection attacks, where an attacker can craft malicious commands encapsulated within a generic template that tricks the model into misclassifying them as safe. This misclassification allows the attacker to bypass the user approval mechanism and execute arbitrary commands on the terminal automatically. The flaw is inherent in the AI's command classification logic and the trust placed in the model's judgment without additional verification layers. Although no specific affected versions are listed and no patches or exploits are currently known, the design weakness poses a significant risk to any deployment of AI Code that uses this automatic execution feature. The absence of a CVSS score indicates that the vulnerability is newly published and requires further assessment. The threat highlights the challenges of relying on AI models for security-sensitive decisions, especially when adversaries can manipulate input prompts to subvert intended controls.

CWE-20Improper Input Validation (opens in a new tab)
Class weaknessLikelihood of exploit: HighCWE Top 25 (2025): #18OWASP Top 10 A05:2025: Injection (opens in a new tab)
The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
MITRE discourages mapping vulnerabilities to this broad entry; the root cause is usually a more specific weakness.
Architecture and Design · Attack Surface Reduction
Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of…
Architecture and Design · Libraries or Frameworks
Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).
Architecture and Design · Implementation · Attack Surface Reduction
Understand all the potential areas where untrusted inputs can enter the product, including but not limited to: parameters or arguments, cookies, anything read from the network, environment variables, reverse DNS lookups, query results, request headers, URL components, e-mail, files, filenames, databases, and any…
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
Injection Prevention (opens in a new tab)
SQL Injection Prevention (opens in a new tab)
Injection Prevention in Java (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Buffer Overflow via Environment Variables (opens in a new tab)
Server Side Include (SSI) Injection (opens in a new tab)
Cross Zone Scripting (opens in a new tab)
Command Line Execution through SQL Injection (opens in a new tab)
Object Relational Mapping Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 27 Mar 2026, 14:50 UTC

WeaknessesCWE-20

Technical details

References

Community reviews

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