diff --git a/jax_privacy/auditing.py b/jax_privacy/auditing.py
index 8f01fd4f..01cff0d8 100644
--- a/jax_privacy/auditing.py
+++ b/jax_privacy/auditing.py
@@ -901,30 +901,21 @@ def epsilon_from_gdp(
     if eps_tol <= 0:
       raise ValueError(f'eps_tol must be positive, got {eps_tol}.')
 
-    n_pos = self._fn_counts[-1]
-    n_neg = self._tn_counts[-1]
-
-    n = len(self._fn_counts)
-
-    # Apply Bonferroni correction over 2 * n hypotheses.
-    fnr_ubs = _clopper_pearson_upper(
-        self._fn_counts, n_pos, significance / (2 * n)
+    # Swapping in/out scores audits the D', D direction with its own frontier.
+    _, reverse_tn_counts, reverse_fn_counts = _get_tn_fn_counts(
+        self._out_canary_scores, self._in_canary_scores
+    )
+    bound_significance = significance / (
+        2 * (len(self._fn_counts) + len(reverse_fn_counts))
+    )
+    max_mu = max(
+        self._mu_from_gdp_counts(
+            self._fn_counts, self._tn_counts, bound_significance, delta
+        ),
+        self._mu_from_gdp_counts(
+            reverse_fn_counts, reverse_tn_counts, bound_significance, delta
+        ),
     )
-    fp_counts = n_neg - self._tn_counts
-    fpr_ubs = _clopper_pearson_upper(fp_counts, n_neg, significance / (2 * n))
-
-    bounds = np.stack([fpr_ubs, fnr_ubs], axis=1)
-
-    # Filter any thresholds where TNR or TPR is too small.
-    bounds = bounds[np.max(bounds, axis=1) < 1 - delta]
-    if not bounds.size:
-      return 0
-
-    # Eq. 6 in https://arxiv.org/abs/1905.02383. If FPR + FNR is too large,
-    # the bound still holds in reverse (by switching D and D'), which has the
-    # effect of making mu from Eq. 6 negative. Hence we look for the maximum
-    # absolute value of mu.
-    max_mu = np.max(np.abs(_norm.isf(bounds[:, 0]) - _norm.ppf(bounds[:, 1])))
     if max_mu == 0:
       return 0
 
@@ -944,6 +935,30 @@ def delta_gap(eps):
 
     return scipy.optimize.brentq(delta_gap, eps_lb, eps_ub, xtol=eps_tol)
 
+  @staticmethod
+  def _mu_from_gdp_counts(
+      fn_counts: np.ndarray,
+      tn_counts: np.ndarray,
+      bound_significance: float,
+      delta: float,
+  ) -> float:
+    """Calculates a one-sided GDP lower bound on mu from one frontier."""
+    n_pos = fn_counts[-1]
+    n_neg = tn_counts[-1]
+
+    fnr_ubs = _clopper_pearson_upper(fn_counts, n_pos, bound_significance)
+    fpr_ubs = _clopper_pearson_upper(
+        n_neg - tn_counts, n_neg, bound_significance
+    )
+
+    keep = np.maximum(fpr_ubs, fnr_ubs) < 1 - delta
+    if not np.any(keep):
+      return 0.0
+
+    # Eq. 6 in https://arxiv.org/abs/1905.02383 gives
+    # mu >= isf(FPR) - ppf(FNR). Negative values are no evidence.
+    return max(0.0, np.max(_norm.isf(fpr_ubs[keep]) - _norm.ppf(fnr_ubs[keep])))
+
   def _epsilon_one_run_all_thresholds(
       self,
       significance: float,
diff --git a/tests/auditing_test.py b/tests/auditing_test.py
index 31eeaadb..a2078aa8 100644
--- a/tests/auditing_test.py
+++ b/tests/auditing_test.py
@@ -607,6 +607,54 @@ def test_epsilon_from_gdp_tight(self, mu, out_samples_ratio):
     true_eps = dp_accounting.get_epsilon_gaussian(1 / mu, delta)
     np.testing.assert_allclose(eps, true_eps, rtol=0.05)
 
+  def test_epsilon_from_gdp_null_is_zero(self):
+    rng = np.random.default_rng(seed=0xBAD5EED)
+    significance = 0.05
+    delta = 1e-5
+    m = 5000
+    in_canary_scores = rng.normal(0, 1, m)
+    out_canary_scores = rng.normal(0, 1, m)
+    auditor = auditing.CanaryScoreAuditor(in_canary_scores, out_canary_scores)
+
+    eps = auditor.epsilon_from_gdp(significance, delta)
+    self.assertLessEqual(eps, 0.1)
+
+  def test_epsilon_from_gdp_separated_is_positive(self):
+    rng = np.random.default_rng(seed=0xBAD5EED)
+    significance = 0.05
+    delta = 1e-5
+    m = 5000
+    in_canary_scores = rng.normal(3.0, 1, m)
+    out_canary_scores = rng.normal(0, 1, m)
+    auditor = auditing.CanaryScoreAuditor(in_canary_scores, out_canary_scores)
+
+    eps = auditor.epsilon_from_gdp(significance, delta)
+    self.assertGreater(eps, 1.0)
+
+  def test_epsilon_from_gdp_reverse_separated_is_positive(self):
+    rng = np.random.default_rng(seed=0xBAD5EED)
+    significance = 0.05
+    delta = 1e-5
+    m = 5000
+    in_canary_scores = rng.normal(0.0, 1, m)
+    out_canary_scores = rng.normal(3.0, 1, m)
+    auditor = auditing.CanaryScoreAuditor(in_canary_scores, out_canary_scores)
+
+    eps = auditor.epsilon_from_gdp(significance, delta)
+    self.assertGreater(eps, 1.0)
+
+  def test_epsilon_from_gdp_small_sample_null_is_zero(self):
+    rng = np.random.default_rng(seed=0xC0FFEE)
+    significance = 0.05
+    delta = 1e-5
+    m = 50
+    in_canary_scores = rng.normal(0, 1, m)
+    out_canary_scores = rng.normal(0, 1, m)
+    auditor = auditing.CanaryScoreAuditor(in_canary_scores, out_canary_scores)
+
+    eps = auditor.epsilon_from_gdp(significance, delta)
+    self.assertLessEqual(eps, 0.1)
+
   @parameterized.product(
       quantiles=(0.025, 0.975, (0.025, 0.975), (0.025, 0.5, 0.975)),
       bootstrap_type=('quantile', 'bias_correction', 'acceleration'),