diff --git a/Eigen/src/Core/DenseBase.h b/Eigen/src/Core/DenseBase.h
index 005f68a..50922a6 100644
--- a/Eigen/src/Core/DenseBase.h
+++ b/Eigen/src/Core/DenseBase.h
@@ -405,6 +405,8 @@
       *
       * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
       * a const reference, in order to avoid a useless copy.
+      * 
+      * \warning Be carefull with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
       */
     EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE EvalReturnType eval() const
diff --git a/doc/Pitfalls.dox b/doc/Pitfalls.dox
index 203843c..cf42eff 100644
--- a/doc/Pitfalls.dox
+++ b/doc/Pitfalls.dox
@@ -18,5 +18,21 @@
 
 In this example, the type of C is not a MatrixXd but an abstract expression representing a matrix product and storing references to A and B. Therefore, the product of A*B will be carried out multiple times, once per iteration of the for loop. Moreover, if the coefficients of A or B change during the iteration, then C will evaluate to different values.
 
+Here is another example leading to a segfault:
+\code
+auto C = ((A+B).eval()).transpose();
+// do something with C
+\endcode
+The problem is that eval() returns a temporary object (in this case a MatrixXd) which is then referenced by the Transpose<> expression. However, this temporary is deleted right after the first line, and there the C expression reference a dead object. The same issue might occur when sub expressions are automatically evaluated by Eigen as in the following example:
+\code
+VectorXd u, v;
+auto C = u + (A*v).normalized();
+// do something with C
+\endcode
+where the normalized() method has to evaluate the expensive product A*v to avoid evaluating it twice. On the other hand, the following example is perfectly fine:
+\code
+auto C = (u + (A*v).normalized()).eval();
+\endcode
+In this case, C will be a regular VectorXd object.
 */
 }