Vulnerability-Lookup

CVE-2026-70477 (GCVE-0-2026-70477)

Vulnerability from cvelistv5 – Published: 2026-08-04 19:29 – Updated: 2026-08-05 13:51

Title

Flowise: CSV Agent Prompt Injection Remote Code Execution Vulnerability

Summary

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

Severity

9.5 (Critical)


                        
                          CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

SSVC

Exploitation: poc Automatable: yes Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-08-05 13:51 UTC

CWE

CWE-94 - Improper Control of Generation of Code ('Code Injection')

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 8083 records in the last 180 days details

References

4 references

URL	Tags
https://github.com/FlowiseAI/Flowise/security/adv…	x_refsource_CONFIRM
https://github.com/FlowiseAI/Flowise/pull/6499	x_refsource_MISC
https://github.com/FlowiseAI/Flowise/commit/f4e27…	x_refsource_MISC
https://github.com/FlowiseAI/Flowise/releases/tag…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version	CPE status
FlowiseAI	Flowise	Affected: < 3.1.2	guessed

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-70477

Vulnerability from fkie_nvd - Published: 2026-08-04 20:16 - Updated: 2026-10-24 15:10

Severity

9.8 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Summary

References

URL	Tags
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/commit/f4e2794f6a576b94578f2fdafbf49c2fb304626c	Patch
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/pull/6499	Issue Tracking, Patch
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/releases/tag/flowise@3.1.3	Release Notes
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5xvg-pmgg-3mxr	Vendor Advisory
134c704f-9b21-4f2e-91b3-4a467353bcc0	https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5xvg-pmgg-3mxr	Vendor Advisory

Impacted products

	Vendor	Product	Version
	flowiseai	flowise	*

JSON

To clipboard

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        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 9.8,
          "baseSeverity": "CRITICAL",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ],
    "cvssMetricV40": [
      {
        "cvssData": {
          "Automatable": "NOT_DEFINED",
          "Recovery": "NOT_DEFINED",
          "Safety": "NOT_DEFINED",
          "attackComplexity": "HIGH",
          "attackRequirements": "PRESENT",
          "attackVector": "NETWORK",
          "availabilityRequirement": "NOT_DEFINED",
          "baseScore": 9.5,
          "baseSeverity": "CRITICAL",
          "confidentialityRequirement": "NOT_DEFINED",
          "exploitMaturity": "NOT_DEFINED",
          "integrityRequirement": "NOT_DEFINED",
          "modifiedAttackComplexity": "NOT_DEFINED",
          "modifiedAttackRequirements": "NOT_DEFINED",
          "modifiedAttackVector": "NOT_DEFINED",
          "modifiedPrivilegesRequired": "NOT_DEFINED",
          "modifiedSubAvailabilityImpact": "NOT_DEFINED",
          "modifiedSubConfidentialityImpact": "NOT_DEFINED",
          "modifiedSubIntegrityImpact": "NOT_DEFINED",
          "modifiedUserInteraction": "NOT_DEFINED",
          "modifiedVulnAvailabilityImpact": "NOT_DEFINED",
          "modifiedVulnConfidentialityImpact": "NOT_DEFINED",
          "modifiedVulnIntegrityImpact": "NOT_DEFINED",
          "privilegesRequired": "NONE",
          "providerUrgency": "NOT_DEFINED",
          "subAvailabilityImpact": "HIGH",
          "subConfidentialityImpact": "HIGH",
          "subIntegrityImpact": "HIGH",
          "userInteraction": "NONE",
          "valueDensity": "NOT_DEFINED",
          "vectorString": "CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
          "version": "4.0",
          "vulnAvailabilityImpact": "HIGH",
          "vulnConfidentialityImpact": "HIGH",
          "vulnIntegrityImpact": "HIGH",
          "vulnerabilityResponseEffort": "NOT_DEFINED"
        },
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-70477",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "yes"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-08-05T13:51:06.061832Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-08-04T20:16:54.473",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/commit/f4e2794f6a576b94578f2fdafbf49c2fb304626c"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Issue Tracking",
        "Patch"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/pull/6499"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Release Notes"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/releases/tag/flowise@3.1.3"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Vendor Advisory"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5xvg-pmgg-3mxr"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Vendor Advisory"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5xvg-pmgg-3mxr"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-94"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}

GHSA-5XVG-PMGG-3MXR

Vulnerability from github – Published: 2026-08-04 19:29 – Updated: 2026-08-04 19:29

Summary

Flowise: CSV Agent Prompt Injection Remote Code Execution Vulnerability

Details

-- ABSTRACT -------------------------------------

Trend Micro's Zero Day Initiative has identified a vulnerability affecting the following products: Flowise - Flowise

-- VULNERABILITY DETAILS ------------------------ * Version tested: 3.1.1 * Installer file: https://github.com/FlowiseAI/Flowise (npm install flowise@3.1.1) * Platform tested: Ubuntu 25.10

A prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed pyodide environment. An attacker can leverage this to execute arbitrary code in the context of the user running the server.

This vulnerability allows remote attackers to execute arbitrary code on affected installations of Flowise. Authentication is not required to exploit this vulnerability.

The specific flaw exists within the run method of the CSV_Agents class. The issue results from insufficient input sanitization when using untrusted data to construct an LLM prompt. An attacker can leverage this vulnerability to execute code in the context of the service account.

Analysis

When a user makes a query against a chatflow using the CSV Agent node, the run method of the CSV_Agents class is called. This method reads the CSV file, loads a pyodide environment, and uses pandas to extract column names and data types into a dictionary. It then constructs a system prompt using that dictionary and the user's input, and sends this prompt to a configured LLM. The LLM response is stored in a variable named pythonCode. The method then attempts to validate this value using validatePythonCodeForDataFrame from packages/components/src/pythonCodeValidator.ts before evaluating it in pyodide.

The validator relies on a static regex blocklist. It can be bypassed using obfuscation techniques including string concatenation to reconstruct forbidden identifiers, chr() encoding, aliasing of dangerous builtins, __getattribute__ with concatenated attribute names, frame object inspection, MRO traversal, df.query() expression evaluation, and decorator syntax to invoke exec indirectly. Furthermore, pyodide is not sandboxed from the host operating system, so any Python code that passes the validator is executed with full access to OS interfaces.

From packages/components/nodes/agents/CSVAgent/CSVAgent.ts:

let pythonCode = ''
if (dataframeColDict) {
    const chain = new LLMChain({
        llm: model,
        prompt: PromptTemplate.fromTemplate(systemPrompt),
        verbose: process.env.DEBUG === 'true' ? true : false
    })
    const inputs = {
        dict: dataframeColDict,
        question: input // user-controlled input substituted into prompt
    }
    const res = await chain.call(inputs, [loggerHandler, ...callbacks])
    pythonCode = res?.text // LLM response assigned to pythonCode
    pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '')
}

let finalResult = ''
if (pythonCode) {
    const validation = validatePythonCodeForDataFrame(pythonCode) // blocklist validation applied
    if (!validation.valid) {
        throw new Error(
            `Generated code was rejected for security reasons (${
                validation.reason ?? 'unsafe construct'
            }). Please rephrase your question to use only pandas DataFrame operations.`
        )
    }
    try {
        const code = `import pandas as pd\nimport numpy as np\n${pythonCode}`
        finalResult = await pyodide.runPythonAsync(code) // executed in unsandboxed pyodide
    } catch (error) {
        throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using following code: "${pythonCode}"`)
    }
}

An unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may use prompt injection to cause the LLM to respond with a malicious Python script. An authenticated attacker may instead configure a chatflow that points to an attacker-controlled server, which responds to LLM requests with an attacker-controlled Python payload, bypassing the LLM entirely.

Eight bypass variants were demonstrated against the validator:

Variant	Technique	Bypasses
0	`@exec` decorator with string-concatenated `__import__`	`/\bexec\s\(/`, `/\b__import__\s\(/`
1	`eval` aliased to a variable, payload chr()-encoded	`/\beval\s*\(/`, `/\bimport\b/`
2	`df.query()` with chr()-encoded `@__builtins__.__import__`	`/\b__builtins__\b/`, `/\b__import__\s*\(/`
3	MRO traversal + `__getattribute__` + `__subclasses__` -> `BuiltinImporter.load_module`	`/\b__class__\b/`, `/\b__subclasses__\s*\(/`, `/\b__mro__\b/`
4	Generator frame inspection via `gi_frame.f_globals['__loader__']`	`/\b__loader__\b/`, `/\b__globals__\b/`
5	Exception traceback frame walk to `f_builtins['__import__']`	`/\b__globals__\b/`, `/\b__import__\s*\(/`
6	`__build_class__.__self__.__getattribute__('__import__')`	`/\b__import__\s*\(/`
7	`vars` aliased to a variable, `__builtins__` accessed via dict key	`/\bvars\s\(/`, `/\b__builtins__\b/`, `/\b__import__\s\(/`

Repro

The proof of concept (poc.py) has three modes of operation:

mode = "server": Starts a malicious server that responds to "/api/chat" requests with a JSON object containing an LLM response with the selected attack payload.

mode = "chatflow": Authenticates to the Flowise server, creates a chatflow with a CSV Agent node configured to use a ChatOllama model pointed at the malicious server, and triggers a prediction to execute the payload.

mode = "prompt_injection": Sends a prompt injection payload directly to an existing chatflow's prediction endpoint. Due to the nature of LLM responses, it may take multiple attempts or require a different injection technique depending on the model used.

python3 poc.py --mode [server OR chatflow OR prompt_injection] [--user <USER> --passwd <PASSWORD> --host <HOST> --r_host <R_HOST> --r_port <R_PORT> --l_port <L_PORT> --port <PORT> --cmd <CMD> --attack <ATTACK> --chatflow_id <CHAT_ID>]

-- CREDIT --------------------------------------- This vulnerability was discovered by: Dre Cura (@dre_cura) of TrendAI Research

Severity

9.5 (Critical)


                  
                    CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

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{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 3.1.2"
      },
      "package": {
        "ecosystem": "npm",
        "name": "flowise"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.1.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 3.1.2"
      },
      "package": {
        "ecosystem": "npm",
        "name": "flowise-components"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.1.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-70477"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-94"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-08-04T19:29:26Z",
    "nvd_published_at": null,
    "severity": "CRITICAL"
  },
  "details": "-- ABSTRACT -------------------------------------\n\nTrend Micro\u0027s Zero Day Initiative has identified a vulnerability affecting the following products:\nFlowise - Flowise\n\n-- VULNERABILITY DETAILS ------------------------\n* Version tested: 3.1.1\n* Installer file: https://github.com/FlowiseAI/Flowise (npm install flowise@3.1.1)\n* Platform tested: Ubuntu 25.10\n\n---\n\nA prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed pyodide environment. An attacker can leverage this to execute arbitrary code in the context of the user running the server.\n\n```\nThis vulnerability allows remote attackers to execute arbitrary code on affected installations of Flowise. Authentication is not required to exploit this vulnerability.\n\nThe specific flaw exists within the run method of the CSV_Agents class. The issue results from insufficient input sanitization when using untrusted data to construct an LLM prompt. An attacker can leverage this vulnerability to execute code in the context of the service account.\n```\n\n### Analysis\n\nWhen a user makes a query against a chatflow using the CSV Agent node, the `run` method of the `CSV_Agents` class is called. This method reads the CSV file, loads a pyodide environment, and uses pandas to extract column names and data types into a dictionary. It then constructs a system prompt using that dictionary and the user\u0027s input, and sends this prompt to a configured LLM. The LLM response is stored in a variable named `pythonCode`. The method then attempts to validate this value using `validatePythonCodeForDataFrame` from `packages/components/src/pythonCodeValidator.ts` before evaluating it in pyodide.\n\nThe validator relies on a static regex blocklist. It can be bypassed using obfuscation techniques including string concatenation to reconstruct forbidden identifiers, `chr()` encoding, aliasing of dangerous builtins, `__getattribute__` with concatenated attribute names, frame object inspection, MRO traversal, `df.query()` expression evaluation, and decorator syntax to invoke `exec` indirectly. Furthermore, pyodide is not sandboxed from the host operating system, so any Python code that passes the validator is executed with full access to OS interfaces.\n\nFrom `packages/components/nodes/agents/CSVAgent/CSVAgent.ts`:\n```ts\nlet pythonCode = \u0027\u0027\nif (dataframeColDict) {\n    const chain = new LLMChain({\n        llm: model,\n        prompt: PromptTemplate.fromTemplate(systemPrompt),\n        verbose: process.env.DEBUG === \u0027true\u0027 ? true : false\n    })\n    const inputs = {\n        dict: dataframeColDict,\n        question: input // user-controlled input substituted into prompt\n    }\n    const res = await chain.call(inputs, [loggerHandler, ...callbacks])\n    pythonCode = res?.text // LLM response assigned to pythonCode\n    pythonCode = pythonCode.replace(/^```[a-z]+\\n|\\n```$/gm, \u0027\u0027)\n}\n\nlet finalResult = \u0027\u0027\nif (pythonCode) {\n    const validation = validatePythonCodeForDataFrame(pythonCode) // blocklist validation applied\n    if (!validation.valid) {\n        throw new Error(\n            `Generated code was rejected for security reasons (${\n                validation.reason ?? \u0027unsafe construct\u0027\n            }). Please rephrase your question to use only pandas DataFrame operations.`\n        )\n    }\n    try {\n        const code = `import pandas as pd\\nimport numpy as np\\n${pythonCode}`\n        finalResult = await pyodide.runPythonAsync(code) // executed in unsandboxed pyodide\n    } catch (error) {\n        throw new Error(`Sorry, I\u0027m unable to find answer for question: \"${input}\" using following code: \"${pythonCode}\"`)\n    }\n}\n```\n\nAn unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may use prompt injection to cause the LLM to respond with a malicious Python script. An authenticated attacker may instead configure a chatflow that points to an attacker-controlled server, which responds to LLM requests with an attacker-controlled Python payload, bypassing the LLM entirely.\n\nEight bypass variants were demonstrated against the validator:\n\n| Variant | Technique | Bypasses |\n|---------|-----------|----------|\n| 0 | `@exec` decorator with string-concatenated `__import__` | `/\\bexec\\s*\\(/`, `/\\b__import__\\s*\\(/` |\n| 1 | `eval` aliased to a variable, payload chr()-encoded | `/\\beval\\s*\\(/`, `/\\bimport\\b/` |\n| 2 | `df.query()` with chr()-encoded `@__builtins__.__import__` | `/\\b__builtins__\\b/`, `/\\b__import__\\s*\\(/` |\n| 3 | MRO traversal + `__getattribute__` + `__subclasses__` -\u003e `BuiltinImporter.load_module` | `/\\b__class__\\b/`, `/\\b__subclasses__\\s*\\(/`, `/\\b__mro__\\b/` |\n| 4 | Generator frame inspection via `gi_frame.f_globals[\u0027__loader__\u0027]` | `/\\b__loader__\\b/`, `/\\b__globals__\\b/` |\n| 5 | Exception traceback frame walk to `f_builtins[\u0027__import__\u0027]` | `/\\b__globals__\\b/`, `/\\b__import__\\s*\\(/` |\n| 6 | `__build_class__.__self__.__getattribute__(\u0027__import__\u0027)` | `/\\b__import__\\s*\\(/` |\n| 7 | `vars` aliased to a variable, `__builtins__` accessed via dict key | `/\\bvars\\s*\\(/`, `/\\b__builtins__\\b/`, `/\\b__import__\\s*\\(/` |\n\n### Repro\n\nThe proof of concept (`poc.py`) has three modes of operation:\n\n**mode = \"server\"**: Starts a malicious server that responds to \"/api/chat\" requests with a JSON object containing an LLM response with the selected attack payload.\n\n**mode = \"chatflow\"**: Authenticates to the Flowise server, creates a chatflow with a CSV Agent node configured to use a ChatOllama model pointed at the malicious server, and triggers a prediction to execute the payload.\n\n**mode = \"prompt_injection\"**: Sends a prompt injection payload directly to an existing chatflow\u0027s prediction endpoint. Due to the nature of LLM responses, it may take multiple attempts or require a different injection technique depending on the model used.\n\n```\npython3 poc.py --mode [server OR chatflow OR prompt_injection] [--user \u003cUSER\u003e --passwd \u003cPASSWORD\u003e --host \u003cHOST\u003e --r_host \u003cR_HOST\u003e --r_port \u003cR_PORT\u003e --l_port \u003cL_PORT\u003e --port \u003cPORT\u003e --cmd \u003cCMD\u003e --attack \u003cATTACK\u003e --chatflow_id \u003cCHAT_ID\u003e]\n```\n\n\n-- CREDIT ---------------------------------------\nThis vulnerability was discovered by:\nDre Cura (@dre_cura) of TrendAI Research",
  "id": "GHSA-5xvg-pmgg-3mxr",
  "modified": "2026-08-04T19:29:26Z",
  "published": "2026-08-04T19:29:26Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5xvg-pmgg-3mxr"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/pull/6499"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/commit/f4e2794f6a576b94578f2fdafbf49c2fb304626c"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/FlowiseAI/Flowise"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/releases/tag/flowise@3.1.3"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Flowise: CSV Agent Prompt Injection Remote Code Execution Vulnerability"
}

WID-SEC-W-2026-2589

Vulnerability from csaf_certbund - Published: 2026-07-29 22:00 - Updated: 2026-08-04 22:00

Summary

Flowise: Mehrere Schwachstellen

Severity

Hoch

Scope

22 vulnerabilities in this advisory, including CVE-2026-70477.

CVE-2026-70477

Known affected: 1 product

Open the full advisory

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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CVE-2026-70477 (GCVE-0-2026-70477)

FKIE_CVE-2026-70477

GHSA-5XVG-PMGG-3MXR

Analysis

Repro

WID-SEC-W-2026-2589

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