Critical SSRF Bug (CVE-2025-62616) Plagues AutoGPT Platforms

Overview/Introduction

On February 5, 2026, security researchers at OffSeq published a detailed analysis of a critical SSRF flaw affecting the Significant-Gravitas AutoGPT platform, a rapidly adopted framework for building autonomous AI agents. The vulnerability, cataloged as CVE-2025-62616, resides in the SendDiscordFileBlock component and allows an attacker to supply an arbitrary URL that the AutoGPT server will fetch using the aiohttp library. Because the URL is not validated, the server can be coerced into reaching internal endpoints, cloud metadata services, or any reachable network resource.

This flaw is especially alarming given the growing reliance on AutoGPT for mission-critical workflow automation across finance, healthcare, manufacturing, and governmental sectors. The vulnerability is classified as critical with a CVSS 4.0 base score of 9.3, reflecting its ease of exploitation (no authentication required) and the high impact on confidentiality, integrity, and availability.

Technical Details (CVE, Attack Vector, Exploitation Method)

The root cause lies in the SendDiscordFileBlock implementation, which constructs an aiohttp.ClientSession().get(url) call directly from user-supplied input. The code snippet (simplified) looks like this:

async def send_file(url: str): async with aiohttp.ClientSession() as session: async with session.get(url) as resp: data = await resp.read() # ... forward data to Discord ...

There is no whitelist, no scheme validation, and no check against private IP ranges or internal hostnames. An attacker can therefore craft a payload such as:

{"url": "http://169.254.169.254/latest/meta-data/iam/security-credentials/"}

When the AutoGPT instance processes this block, it contacts the cloud provider’s metadata service, retrieves IAM credentials, and returns them to the attacker (or stores them for later exfiltration). Because the request originates from the AutoGPT server, it bypasses perimeter firewalls and any external network segmentation.

Exploitation steps are straightforward:

• Identify a running AutoGPT instance (any public endpoint that accepts a task definition containing a SendDiscordFileBlock).
• Submit a job with a malicious URL pointing to an internal service (e.g., http://10.0.0.5:8080/admin or the cloud metadata endpoint).
• Capture the response via the AutoGPT output channel (Discord webhook, console log, or API response).
• Leverage the retrieved data to pivot further-e.g., use leaked credentials for remote code execution (RCE) against downstream services.

The vulnerability is present in all releases prior to autogpt-platform-beta-v0.6.34. The patched version adds strict URL validation, limits outbound requests to whitelisted domains, and disables access to private IP ranges by default.

Impact Analysis (Who Is Affected, How Severe)

Any organization deploying Significant-Gravitas AutoGPT-whether on-premise, in a private cloud, or using the vendor’s managed SaaS-faces the same exposure. The impact can be broken down into three tiers:

1. Data Confidentiality: Attackers can retrieve configuration files, internal API keys, database connection strings, or cloud metadata, leading to credential leakage.
2. Network Reconnaissance: By probing internal services (e.g., http://10.0.0.1:9200 for Elasticsearch), adversaries can map the internal topology, discover unpatched services, and plan lateral movement.
3. Chaining to Remote Code Execution: If the internal service accepts file uploads or command execution (e.g., a vulnerable Jenkins instance), the SSRF can be a stepping stone to full RCE, compromising the entire environment.

Regulatory implications are also severe. In the EU, exposure of personal data via a breached internal service can trigger GDPR penalties, while US-based sectors such as finance may violate PCI-DSS or HIPAA requirements.

Timeline of Events

• 2025-11-15 - Initial internal bug report filed by a developer during code review (not publicly disclosed).
• 2025-12-02 - Independent security researcher discovers the SSRF while fuzzing the SendDiscordFileBlock endpoint.
• 2026-01-20 - Vendor acknowledges the issue and begins internal remediation.
• 2026-02-04 - Vendor releases security advisory, assigns CVE-2025-62616, and publishes a temporary mitigation guide.
• 2026-02-05 - OffSeq publishes the full technical analysis (this article references that report).
• 2026-02-06 - Patch autogpt-platform-beta-v0.6.34 made available for download and as a forced update for SaaS customers.

Mitigation/Recommendations

While the vendor’s patch is the definitive fix, organizations should adopt a defense-in-depth approach immediately:

• Upgrade Now: Deploy autogpt-platform-beta-v0.6.34 or later across all environments. Verify the version via the CLI (autogpt --version).
• Network Segmentation: Place AutoGPT instances in a separate VLAN with strict egress controls. Block outbound traffic to private IP ranges (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16) and to known metadata endpoints (169.254.169.254).
• Outbound Request Filtering: Deploy a forward proxy that enforces domain whitelisting for HTTP(S) requests originating from AutoGPT. Log all outbound requests for anomaly detection.
• Input Validation in Custom Blocks: Review any custom AutoGPT blocks that perform external calls. Enforce URL schema checks, allowlist domains, and reject IP literals.
• Credential Rotation: Assume that any IAM credentials accessed via the metadata service may be compromised. Rotate keys, secrets, and tokens immediately.
• Monitoring & Detection: Enable alerts for unusual outbound HTTP requests from the AutoGPT host, especially to internal IPs or cloud metadata services.

For SaaS customers who cannot apply patches themselves, contact the vendor’s support team to confirm that the forced update has been applied and request a summary of the egress controls enforced on the managed platform.

Real-World Impact (How This Affects Organizations/Individuals)

Consider a multinational bank that uses AutoGPT to automate compliance reporting. An attacker injects a malicious URL into a routine SendDiscordFileBlock task. The AutoGPT instance, residing behind the bank’s internal firewall, reaches the internal secrets manager and extracts API keys for the transaction processing system. The stolen keys enable the adversary to initiate fraudulent transfers, leading to financial loss, regulatory fines, and reputational damage.

In a healthcare setting, a hospital’s AutoGPT workflow retrieves patient imaging data from an internal PACS server. Exploiting the SSRF, a threat actor can download protected PHI (Protected Health Information), violating HIPAA and exposing the organization to hefty penalties.

Even for smaller startups that host AutoGPT on public cloud VMs, the vulnerability can be used to enumerate cloud resources, discover misconfigured S3 buckets, or pull service account tokens, effectively handing the attacker a foothold in the cloud tenant.

Expert Opinion

From a broader industry perspective, CVE-2025-62616 underscores a recurring theme: AI-driven automation platforms are becoming high-value attack surfaces because they sit at the nexus of internal data, external APIs, and privileged execution contexts. The root cause-a missing URL validation step-might seem trivial, yet the downstream impact is massive.

Moving forward, vendors must treat every integration point (webhooks, file fetchers, third-party SDKs) as a potential attack vector and adopt secure-by-design principles. Automated static analysis tools that flag unsafe HTTP client usage, combined with runtime request-policy enforcement, should become standard in CI/CD pipelines for AI platforms.

For security teams, the lesson is clear: inventory all AI agents, map their outbound connections, and enforce least-privilege network policies. The rapid adoption of generative AI does not give us the luxury of overlooking classic web-application flaws. SSRF, once considered a niche issue, is now a critical risk vector in the AI era.