Common Weakness Enumeration
CWE-1427
AllowedImproper Neutralization of Input Used for LLM Prompting
Abstraction: Base · Status: Incomplete
The product uses externally-provided data to build prompts provided to large language models (LLMs), but the way these prompts are constructed causes the LLM to fail to distinguish between user-supplied inputs and developer provided system directives.
15 vulnerabilities reference this CWE, most recent first.
CVE-2026-75130 (GCVE-0-2026-75130)
Vulnerability from cvelistv5 – Published: 2026-08-18 18:02 – Updated: 2026-08-19 15:14
VLAI
EPSS
VEX
Title
Context7 2.1.2 Prompt Injection via Custom AI Instructions
Summary
Context7 through 2.1.2 contains a prompt injection vulnerability that allows attackers to execute malicious instructions in connected AI coding agents by injecting unsanitized content through the Custom AI Instructions feature served via the MCP server. Attackers can poison the custom instructions to exfiltrate credentials from environment files to an attacker-controlled service and perform destructive file deletion on the victim's machine when the agent makes a routine library documentation request.
Severity
9 (Critical)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-19 15:14 UTC
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://noma.security/blog/contextcrush-context7-… | technical-descriptionexploit |
| https://www.linkedin.com/posts/upstash_mcp-server… | vendor-advisory |
| https://www.vulncheck.com/advisories/context7-pro… | third-party-advisory |
Date Public
2026-03-05 00:00
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CVE-2026-46580 (GCVE-0-2026-46580)
Vulnerability from cvelistv5 – Published: 2026-06-18 14:26 – Updated: 2026-06-19 03:55
VLAI
EPSS
VEX
Summary
In Eclipse Theia versions prior to 1.71.0, files matching the pattern .prompts/*.prompttemplate in a workspace were automatically loaded and could override or extend the AI agent's system prompts. An attacker could craft a malicious repository containing prompt template files that, when the workspace was opened in Theia, replaced the AI's system instructions with attacker-controlled content (indirect prompt injection). Combined with other AI chat features available in untrusted workspaces, this enabled attack chains leading to data exfiltration via Markdown image rendering or arbitrary command execution via task definitions.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 00:00 UTC
CWE
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Eclipse Foundation | Eclipse Theia |
Affected:
0 , < 1.71.0
(semver)
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CVE-2026-44717 (GCVE-0-2026-44717)
Vulnerability from cvelistv5 – Published: 2026-05-15 16:58 – Updated: 2026-05-15 17:25
VLAI
EPSS
VEX
Title
MCP Calculate Server: Prompt Injection to RCE
Summary
MCP Calculate Server is a mathematical calculation service based on MCP protocol and SymPy library. Prior to 0.1.1, the use of eval() to evaluate mathematical expressions without proper input sanitization leads to remote code execution. This vulnerability is fixed in 0.1.1.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-15 17:24 UTC
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/611711Dark/mcp_calculate_serve… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| 611711Dark | mcp_calculate_server |
Affected:
< 0.1.1
|
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CVE-2026-44688 (GCVE-0-2026-44688)
Vulnerability from cvelistv5 – Published: 2026-06-18 14:22 – Updated: 2026-06-19 03:55
VLAI
EPSS
VEX
Summary
In Eclipse Theia versions prior to 1.71.0, the AI chat agent processed workspace file and directory names as part of its prompt context without distinguishing them from system instructions. An attacker could craft a malicious repository with adversarial directory or file names that, when analyzed by the AI agent, would cause the agent to follow attacker-controlled instructions (indirect prompt injection). Combined with other AI chat features available in untrusted workspaces, this enabled attack chains leading to data exfiltration via Markdown image rendering or arbitrary command execution via task definitions.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 00:00 UTC
CWE
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Eclipse Foundation | Eclipse Theia |
Affected:
0 , < 1.71.0
(semver)
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CVE-2026-44246 (GCVE-0-2026-44246)
Vulnerability from cvelistv5 – Published: 2026-05-12 20:41 – Updated: 2026-05-13 14:23
VLAI
EPSS
VEX
Title
nnU-Net: Agentic workflow injection in `.github/workflows/issue-triage.yml` of `MIC-DKFZ/nnUNet`
Summary
nnU-Net is a semantic segmentation framework that automatically adapts its pipeline to a dataset. Prior to 2.4.1, the nnU-Net Issue Triage workflow in .github/workflows/issue-triage.yml is vulnerable to Agentic Workflow Injection. The workflow sets allowed_non_write_users: ${{ github.event.issue.user.login }}, which means any logged-in GitHub user who opens an issue can reach this agentic workflow with attacker-controlled content. Untrusted issue title and body content are embedded directly into the prompt of anthropics/claude-code-action, and the workflow then runs a command-capable Claude agent with permission to comment on and relabel the current issue via gh. Because this workflow is triggered automatically on issues.opened, an external attacker can submit a crafted issue that steers the agent beyond its intended issue-triage purpose and influences authenticated issue actions. This vulnerability is fixed in 2.4.1.
Severity
7.2 (High)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-13 14:23 UTC
CWE
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/MIC-DKFZ/nnUNet/security/advis… | x_refsource_CONFIRM |
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CVE-2026-21832 (GCVE-0-2026-21832)
Vulnerability from cvelistv5 – Published: 2026-08-13 13:24 – Updated: 2026-08-13 15:51
VLAI
EPSS
VEX
Title
HCL AION is affected by multiple security vulnerabilities.
Summary
HCL AION is affected by a vulnerability where indirect prompt injection can lead to HTML injection in rendered output. Injected markup may be displayed to users, potentially resulting in unintended behavior or security impact under certain conditions.
Severity
4.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-13 15:51 UTC
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| HCL Software | AION |
Affected:
v2.5.0
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CVE-2026-18733 (GCVE-0-2026-18733)
Vulnerability from cvelistv5 – Published: 2026-08-03 20:33 – Updated: 2026-08-04 19:31
VLAI
EPSS
VEX
Title
Prompt injection bypasses shell tool consent gate in Strands Agents Tools
Summary
A prompt injection vulnerability in the shell tool in Amazon Strands Agents Tools before 0.8.0 might allow remote actors to execute arbitrary operating system commands on the agent's host via a crafted prompt that sets the non_interactive parameter to true, bypassing the human consent gate.
To remediate this issue, users should upgrade to version 0.8.0.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-04 19:31 UTC
CWE
- CWE-1427 - Improper neutralization of input used for LLM prompting
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://pypi.org/project/strands-agents-tools/0.8.0/ | release-notespatch |
| https://aws.amazon.com/security/security-bulletin… | vendor-advisory |
| https://github.com/strands-agents/tools/security/… | release-notes |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| AWS | strands-agents-tools |
Affected:
0 , < 0.8.0
(custom)
cpe:2.3:a:aws:strands-agents-tools:*:*:*:*:*:*:*:* |
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CVE-2026-15077 (GCVE-0-2026-15077)
Vulnerability from cvelistv5 – Published: 2026-07-29 19:00 – Updated: 2026-07-29 19:33
VLAI
EPSS
VEX
Title
Improper Neutralization of Input Used for LLM Prompting in GitLab
Summary
GitLab has remediated an issue in GitLab EE affecting all versions from 19.1 before 19.1.3 and 19.2 before 19.2.1 that under certain conditions could have allowed an authenticated user to access information from unauthorized projects due to improper neutralization of untrusted content processed by the AI-assisted code review functionality.
Severity
4.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-29 19:33 UTC
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Assigner
References
Impacted products
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CVE-2026-4399 (GCVE-0-2026-4399)
Vulnerability from cvelistv5 – Published: 2026-03-31 10:10 – Updated: 2026-03-31 13:31
VLAI
EPSS
VEX
Title
Multiple vulnerabilities in 1millionbot Millie chatbot
Summary
Prompt injection vulnerability in 1millionbot Millie chatbot that occurs when a user manages to evade chat restrictions using Boolean prompt injection techniques (formulating a question in such a way that, upon receiving an affirmative response ('true'), the model executes the injected instruction), causing it to return prohibited information and information outside its intended context. Successful exploitation of this vulnerability could allow a malicious remote attacker to abuse the service for purposes other than those originally intended, or even execute out-of-context tasks using 1millionbot's resources and/or OpenAI's API key. This allows the attacker to evade the containment mechanisms implemented during LLM model training and obtain responses or chat behaviors that were originally restricted.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-31 13:30 UTC
CWE
- CWE-1427 - Improper neutralization of input used for LLM prompting
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| 1millionbot | Millie chat |
Affected:
3.6.0
(custom)
cpe:2.3:a:1millionbot:millie_chat:3.6.0:*:*:*:*:*:*:* |
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CVE-2025-64321 (GCVE-0-2025-64321)
Vulnerability from cvelistv5 – Published: 2025-11-04 18:30 – Updated: 2025-11-11 05:09
VLAI
EPSS
VEX
Summary
Improper Neutralization of Input Used for LLM Prompting vulnerability in Salesforce Agentforce Vibes Extension allows Manipulating Writeable Configuration Files.This issue affects Agentforce Vibes Extension: before 3.3.0.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-11-05 14:31 UTC
CWE
- CWE-1427 - Improper Neutralization of Input Used for LLM Prompting
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Salesforce | Agentforce Vibes Extension |
Affected:
0 , < 3.3.0
(semver)
cpe:2.3:a:salesforce:agentforce_vibes_extension:*:*:*:*:*:*:*:* |
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Mitigation
Architecture and Design
LLM-enabled applications should be designed to ensure proper sanitization of user-controllable input, ensuring that no intentionally misleading or dangerous characters can be included. Additionally, they should be designed in a way that ensures that user-controllable input is identified as untrusted and potentially dangerous.
Mitigation
Implementation
LLM prompts should be constructed in a way that effectively differentiates between user-supplied input and developer-constructed system prompting to reduce the chance of model confusion at inference-time.
Mitigation
Architecture and Design
LLM-enabled applications should be designed to ensure proper sanitization of user-controllable input, ensuring that no intentionally misleading or dangerous characters can be included. Additionally, they should be designed in a way that ensures that user-controllable input is identified as untrusted and potentially dangerous.
Mitigation
Implementation
Ensure that model training includes training examples that avoid leaking secrets and disregard malicious inputs. Train the model to recognize secrets, and label training data appropriately. Note that due to the non-deterministic nature of prompting LLMs, it is necessary to perform testing of the same test case several times in order to ensure that troublesome behavior is not possible. Additionally, testing should be performed each time a new model is used or a model's weights are updated.
Mitigation
Installation
Operation
During deployment/operation, use components that operate externally to the system to monitor the output and act as a moderator. These components are called different terms, such as supervisors or guardrails.
Mitigation
System Configuration
During system configuration, the model could be fine-tuned to better control and neutralize potentially dangerous inputs.
No CAPEC attack patterns related to this CWE.